DEV Community: Aristek Systems

Data warehouse vs data lake

Aristek Systems — Tue, 28 May 2024 13:56:31 +0000

Learn the difference between a data warehouse vs data lake. Everything you need to know in one article.

TLDR: data warehouse vs data lake

If you only have 30 seconds, just read the TLDR. But the devil is in the details, so if you have a few minutes, keep on reading – it’s worth it.

Data warehouse stores processed data that’s ready for analytics. Before data is moved to the warehouse, it gets structured and cleaned. That’s why even non-technical users can analyze data from a warehouse. For example, management can create charts with BI tools.

Data lake is a storage for raw data. You can move any files there and process them later. The idea is that you never know what data will be useful for analytics in 5 years. And because storage today is cheaper than processing, you can collect a ton of data first and maybe process it later. But data still needs to be processed before it’s analyzed. So data lakes can deliver more insights, but they are harder to work with.

Data lakehouse offers the best of both worlds. Technologically, a lakehouse is similar to a data lake but has a predefined schema and stores more metadata.

What’s common between data lakes & warehouses

Let’s get some context first. Why do we even need data warehouses and data lakes when there are regular databases?

A typical database is made for OLTP (online transactional processing). Meanwhile, both data warehouses and data lakes are great for OLAP (analytical processing).

Here’s what it means, in general:

Data integration

A database is much smaller. Think gigabytes, compared to terabytes or petabytes in lakes and warehouses. That is because databases store compact transactional data.

Meanwhile, data warehouses can merge data from many different databases. Data lakes go even further and can store all sorts of unstructured data: JSON files, text files, and even images or videos.

Historical information

In a database, there’s often no track of it because logs don’t keep everything. Databases can track historical data, but this would slow down the transaction process. That’s why databases mostly just keep current info.

If your client changes their address, the database will update to the new address, but the old one will be lost. Without historical info, you won’t see the trends.

Data warehouses and lakes solve this problem because they capture changes in data.

Query complexity

Both data lakes and warehouses are great at complex queries, while databases are made for simple queries.

Databases are pretty bad at complex queries. They rely on normalized schemas, so data is kept in tables. This makes databases very fast at simple queries, like retrieving an order by its ID. But when it comes to complex queries, there are too many joins from different tables – so complex queries become too slow.

Meanwhile, data warehouses and data lakes are great at them because they use denormalized schemas. There are fact tables that hold the measurable data, and dimension tables that contain descriptive attributes. This way, there are much fewer joins.

So if you want to analyze sales trends, you’ll need a data lake or a warehouse. But if you need to keep track of order statuses or process payments, databases are more efficient.

When to get a data warehouse

Simply put, a data warehouse is a giant database that’s optimized for analytics. It aggregates structured data from multiple sources and it’s great for deep analytical reports.

Companies have many different sources of data: databases, API connections with third-party tools or simply CSV files. A data warehouse aggregates all of your operational data in a single place. This way you can automatically create complex reports. You can check how your website redesign affected traffic and sales – with no need to manually pull data from Salesforce or Google Analytics.

Data warehouses are built so that low-technical users can create no-code reports. Data is moved from the sources into a single warehouse, where it can be analyzed with a BI tool.

How do data warehouses work?

Most data warehouses go through the ETL process: extract, transform, load. Here’s how it works:

Extract. Raw data gets extracted to a staging area either through direct querying or via API.
Transform. The data gets cleansed from duplicates and errors. It also gets filtered, sorted and applied with the business rules.
Load. Finally, data is loaded to the warehouse for storage and analysis. Here, it’s organized into tables or data structures for easy access and querying.

Data warehouses have predefined schemas. It means that data engineers had to think in advance about how data is stored at a warehouse. It also means that there are limits on the types of data that get analyzed. To store unstructured raw data, you need a data lake.

When to get a data lake

A data lake is basically a giant pool of raw data. Sometimes the data here is structured, sometimes it’s semi-structured, and often is unstructured at all.

It is perfect for big data projects because a data lake lets you store more information cheaper and faster. With a data lake, data scientists can experiment much more and explore ideas much quicker than with a warehouse.

But unlike data warehouses, managers typically can’t create reports straight out of the lake. It requires technical experts: data scientists and data engineers. That is unless your lake has another layer like Apache Hive.

A data lake is much easier to start with because it has an undefined schema. That’s called schema-on-read because it’s only applied during querying.

But when you process data from a lake, you still need to put it in a data warehouse. That’s right, when you have a data lake, in most cases you also need a data warehouse.

The problem with data lakes is that they can turn into data swamps. It’s tempting for companies to collect every bit of data they can. But it turns out, that’s not always a good idea. Data storage can get too expensive, and data processing becomes too difficult. There’s a joke that says, data lakes are named after Lake Karachay – a nuclear waste dump.

How do data lakes work?

Data lake is like a data warehouse, but backward. So instead of the ETL pipeline, data lakes use ELT: extract, load, transform.

Extract. Just like with a data warehouse, your data gets automatically extracted from all your sources: databases, APIs, IoT devices, etc. It’s possible to retrieve data in bulk or in real time.
Load. Data gets loaded into the data lake and stays there in native format until it’s required. Because data lakes store unstructured data, it’s easier to upload data just in case. The key is that data stays in the lake without any transformation.
Transform. Before analyzing data, it still needs to get transformed. But in ELT, it’s the data scientists who define how to process the data. Just like in ETL, raw data is cleaned, enriched, and converted into a structured format.
Load. Sometimes ELT is actually ELTL, because after transformation data still goes into a data warehouse. But you can also use it for your data science and machine learning projects.

Data lakehouse: best of both worlds

Let’s recap the issues with data lakes and warehouses:

Data warehouses are organized but too expensive for big data and AI projects. That’s because every time your data goes into a warehouse, it needs to be processed and organized. For big data, it gets too difficult. That’s also the reason why data warehouses are not real-time.
Data lakes are cheaper but can turn into data swamps if you collect too much low-quality data: lots of duplicates, inaccurate or incomplete data.

Data lakehouses keep the advantages of both, and remove the disadvantages. They are cost-effective, but scalable and organized. A data lakehouse is great for both predictive and prescriptive analytics.

Your analysts can work with a lakehouse as if it was a regular data warehouse. But your data engineers and data scientists can set up machine learning experiments in the same place – as if it was a data lake.

Real-life example

A lakehouse is great when you need data for both descriptive and predictive analytics. That’s exactly the case with the AI solution we’ve built.

AI-based behavior analysis & sales

How do data lakehouses work?

On the surface, lakehouses look just like regular data lakes with raw data. This data also can be structured, semi-structured, or unstructured.

The difference is that data lakehouses have another layer for metadata and additional governance. This layer keeps metadata of all the objects in the lakehouse. This way, it’s just as easy to organize data as with a regular data warehouse.

Can analytics be collected without a data warehouse or data lake?

Sure. It’s still possible to analyze data without data lakes, warehouses, or lakehouses. But with no proper infrastructure, this process is manual – someone has to go and pull data from every source.

There are some problems with that, though:

No scalability. Data always piles up: companies buy more tools, get more clients, and hire more employees all the time. At some point, it gets impossible for humans to process it all.
Data needs to be updated. Data doesn’t just pile up – it changes, too. Your employees need to update all the spreadsheets, and some spreadsheets always need an update. After a while, you’ll have to let some spreadsheets go.
Privacy and compliance risks. Some data is private by law. Other information is simply not for low-level employees. If you automate your data pipeline, you can easily manage access to your data.
People make mistakes. Our brains are not built for repetitive work. When someone copies and pastes spreadsheets all day, there will inevitably be mistakes. These mistakes can cost a lot.

If you are serious about data analytics, you need proper infrastructure.

How to get modern data infrastructure

You need data engineers and data scientists. The good thing is – once you build your data infrastructure, maintenance is easy.

In most cases, it takes less than a year to set up a data warehouse. Also, if you want to build data infrastructure fast, you’ll need more than one data engineer.

But the more people you hire for development, the more will stay for maintenance. And when everything works well, maintaining infrastructure is not a full-time job. It often doesn’t make much sense to hire in-house data engineers. Especially, since this is a very well-paid position with a median salary of over $150,000.

Outsourcing is cheaper and faster. You can hire several engineers to build data infrastructure quickly. After launch, you can keep just one data engineer part-time for maintenance. But that engineer will know everything about the project because they built it.

We provide data engineering services here at Aristek Systems. We keep only the best data engineers and data scientists on board. They build data infrastructure from scratch or at any point in the project. Reach out for a free consultation.

Our Giant Top PHP Development Companies. Whom to Hire in 2024?

Aristek Systems — Wed, 10 Apr 2024 12:27:55 +0000

There are plenty of PHP developers out there, but it’s hard to hire the right company. This is our list of top PHP development companies. Hopefully, we can help you navigate around the vast ocean of them.

What is PHP & When to Use It?

PHP is the most popular scripting language on the web. 4 out of 5 websites are built with PHP, some of these are Etsy, Yahoo, and Wikipedia.

It’s mostly used for backend development in web applications. Here’s how you can use it:

Forms and authorization. HTML alone is not good with forms. PHP scripts enable websites to collect user data, process orders, or manage user accounts;
Dynamic content. Think of an online shop. Each product page shows the product’s pictures and prices. This is dynamic content. The website can fetch it from a database when you visit the page, dynamically generate the HTML code to display the product details and send it to your browser;
Integration with 3rd party services. Payment gateways, integrations with Facebook or Twitter – all of it can be done with PHP;
Building frameworks. With PHP, developers can create reusable components that make their work faster. But more on that later.

What Are PHP Frameworks

PHP frameworks provide structure and reusable code. In other words, they simplify development and make it cheaper.

Let’s compare it to building a house. Development with frameworks is like having pre-designed plans. You just follow the instructions that tell you where to put the rooms or plumbing. Without a PHP framework, developers need to start with a blank piece of paper and lay out everything manually. For a web application, this includes routing URLs, connecting to databases, and managing user authentication. All of this takes time and your money.

Developers can build custom frameworks or use a ready-made one. The most popular PHP frameworks are Symfony, Laravel, CodeIgniter, Yii, Zend Framework, and CakePHP. Best PHP development companies will work with several frameworks, so they can suggest the best option for your project.

Our Criteria of Top PHP Development Companies

There are no best PHP developers. But there are the best developers for your project.
If you’re building a complex web application in a regulated industry, like healthcare or education, than the development company better know what they are doing. But if it’s just a small online shop for your hobby, there are plenty of cheaper options around.

Our list considers the price points, company reputation, their PHP experience, and industry knowledge. Your job is to find the combination that suits you best, a hard task indeed.

Aristek Systems

Years of experience: 22.

Location: Lithuania, with offices in the USA and UAE.

Pricing range: $50-99 / hour.

Who they are: Aristek Systems is a software development company with 150 employees. We focus on developing software solutions from scratch and provide PHP developers for companies in need of staff augmentation. Our company provides expert developers: 88% are middle and senior tier, 95% have a computer science degree.

PHP frameworks: Symphony, Zend, Laravel, CodeIgniter, Yii.

PHP development experience: PHP is one of the company’s main languages with lots of featured projects.

Industry focus: the company has tons of experience in education, healthcare, and logistics. We know how to develop solutions that fit compliance regulations and local market requirements. With us, clients save budgets not because of cheap rates, but because we do great solutions faster.

Featured PHP projects: in healthcare, we’ve built an EHR for securely storing patient data. In education, we’ve built numerous learning platforms including a major LMS with 10 million users. In logistics, we’ve developed an ERP that automates sales & operations.

BairesDev

Years of experience: 15.

Location: most of the developers are in South America, meaning they work the same hours as the US clients.

Pricing range: $50 – $99 / hour.

Who they are: Bairesdev is a nearshore development company with over 4000 employees. They provide remote developers to companies of all sizes, from startups to Fortune 500 companies. Some of their clients include Google, Salesforce, and Adobe.

PHP frameworks: Laravel, Symfony, CodeIgniter, Yii, CakePHP, Zend.

PHP development services: PHP is not their top priority. Still, Bairesdev has served over 250 clients with some degree of PHP development. While they also create solutions from scratch, it is mostly a nearshore company – meaning that you can hire PHP developers from them to boost your inhouse team.

Featured PHP projects: Bairesdev provided a development team to Forcepoint, a cyber-security company. On top of PHP developers, they provided experts in Drupal, Java, and frontend developers.

Industry focus: the company doesn’t advertise development focus on specific industries. Still, they’ve landed impressive clients in business services, education, finance, healthcare, manufacturing, etc.

Hyperlink InfoSystem

Years of experience: 13.

Location: Headquarters in the US, development center in India.

Pricing range: $25 – $49 / hour.

Who they are: Hyperlink InfoSystem is a software development company with over 1200 in-house developers. Mostly, the company is focused on mobile apps, but they work with PHP as well. Hyperlink Infosystem is ISO certified.

PHP frameworks: Laravel and CodeIgniter. They also cover most CMSs: WordPress, Magento, and more.

PHP development services: the company focuses on mobile, so in terms of PHP they mostly develop websites and provide PHP consultations.

Featured PHP projects: the company has developed over 2200 websites. Most are WordPress and Drupal stores, but they’ve done some API integrations, too.

Industry focus: as a major company, they’ve finished projects in a ton of industries: Education, Healthcare, Fintech, Real Estate, Retail, Travel & Hospitality, Logistics, Food & Restaurant, Game, Entertainment, On-Demand, and Sports. In terms of PHP, most of the focus is on e-commerce.

Hidden Brains

Years of experience: 21.

Location: India, with offices in the USA, UK, Norway and Africa.

Who they are: Hidden Brains is an offshore development company with 700 employees. They provide developers to startups, product companies, agencies, and global enterprises.

Pricing range: $25 – $49 / hour.

PHP frameworks: Laravel and CodeIgniter.

PHP development services: PHP is one of their main languages. The company develops custom web solutions and APIs. They also do maintenance, support, and migrations.

Featured PHP projects: Hidden Brains helped a backyard rental company migrate from a legacy system to a modern PHP solution. In another project, they developed a logistics platform for a major Nigerian provider.

Industry focus: the company doesn’t have a specific industry focus. But Hidden Brains focuses on CRM, ERM, and ERP development. So although they serve clients in 39 industries, real estate or logistics companies will benefit the most.

The Software House

Years of experience: 9.

Location: Poland.

Pricing range: $50 – $99 / hour.

Who they are: The Software House is a development company with 220 employees. They focus on cloud-native solutions for start-ups and enterprises.

PHP frameworks: Symfony and Laravel development.

PHP development services: PHP isn’t the company’s top language, they currently have 13 PHP developers. Yet they hire only the best talent, and only 5% of applicants get the job at the Software House.

Featured PHP projects: The Software House is great at modernizing existing applications. This includes Finland’s biggest employee benefit system with a million users. They also modernized an event ticketing system in Switzerland. For both projects, Software House provided Symfony developers to revamp the backend.

Industry focus: Fintech and events are the top priority at the Software House, but they also develop solutions in IT, travel, real estate, education, and marketing.

Accessto

Years of experience: 13.

Location: Poland.

Pricing range: $50 – $99 / hour.

Who they are: Accessto is a small software development agency that’s fully committed on PHP Symfony web development. For the most part, their services are used by startups.

PHP frameworks: Symfony.

PHP development services: while it’s a small company with only 9 developers, they cover all sorts of PHP services: from improving existing solutions to full-cycle development.

Featured PHP projects: the company has developed 60+ projects, all are PHP based. Some examples include architecture refactoring and scaling of dozens of startups.

Industry focus: the company focuses on its PHP expertise, not on specific industry experience. So their clients are in multiple domains: fintech, education, healthtech, and all sorts of SaaS products.

RabIT Solutions

Years of experience: 10.

Location: Hungary, with an office in California, USA.

Pricing range: $25 – $49 / hour.

Who they are: RabIT Solutions is a software development company with a high focus on staff augmentation. The company has around 50 employees but over 100 projects.

PHP frameworks: Laravel, CodeIgniter, Symfony, CakePHP, and Yii.

PHP development services: PHP is not in the spotlight of the company, they work with other languages too. However, they provide all sorts of PHP services: consulting, development, migration, and even webscraping.

Featured PHP projects: the company has developed a CMS and several online stores with Magento, Shopify and WooCommerce.

Industry focus: none. Since they mostly focus on staff augmentation, the company doesn’t have a specific industry focus. Still, some of their biggest clients are in retail, travel, and logistics.

WPWeb Infotech

Years of experience: 9.

Location: India, with offices in the US and Canada.

Pricing range: <25$ / hour.

Who they are: WPWeb Infotech is a web development company that specializes in ecommerce web apps and developing WordPress plugins.

PHP frameworks: Laravel, Codelgniter, and CakePHP.

PHP development services: WPWEB is specialized in PHP development for smaller businesses. It’s a great option for companies in need of an online store that’s built on a platform like WordPress or Shopify.

Featured PHP projects: the company has built multiple online CRMs, marketplaces, and online shops. WPWeb provides not only services, but they’ve also launched several popular WordPress plugins.

Industry focus: mostly eCommerce, but also media, real estate, betting, and music.

Science Soft

Years of PHP experience: 16.

Location: headquartered in the US, with development centers in Central Europe.

Pricing range: $50-99 / hour.

Who they are: ScienceSoft is a software development company with 750 IT professionals and 3500 projects in their portfolio.

PHP frameworks: Symfony, Laravel, Zend, and CakePHP.

PHP development services: the company does both PHP consulting and development. In consulting, they’ll help you identify bottlenecks and vulnerabilities. As for development, they can build you a powerful product that would adhere to the latest PSR guidelines.

Featured PHP projects: the company has several major PHP projects. One would be Viber, a global messaging app. Other projects include an automated portfolio management system for a FinTech company or an IoT fleet management service.

Industry focus: this is a major company with multiple projects across dozens of industries. In terms of PHP, the most successful projects were in communication, finance, travel, logistics, and education.

XWP

Years of experience: 19.

Location: NY, USA.

Pricing range: $150 – $199 / hour.

Who they are: XWP is a WordPress agency. They do both the design and the tech part. Basically, XWP develops WordPress for major brands. Some of their clients are Google, Forbes, Twitch, and Rolling Stone.

CMS frameworks: WordPress only. But when it comes to WordPress, they are one of the best.

Development services: they design and develop WordPress websites. But they can also migrate your site from another CMS. Finally, they build custom WordPress plugins.

Featured projects: WXP helped Rolling Stone migrate to WordPress along with online data going back to the 60s.

Industry focus: any company that needs a website.

EPAM

Years of experience: 31.

Location: global.

Pricing range: $100-199 / hour.

**Who they are: **EPAM is one of the world’s largest software development companies with 6000 PHP developers. They serve enterprise-grade companies, startups, and SMBs. The latter 2 are served via a dedicated platform called, well, EPAM Startups & SMB. This platform offers remote developers only.

PHP frameworks: Laravel, Symfony, CodeIgniter, CakePHP, and probably any other available framework.

PHP development experience: EPAM develops software for global enterprises. EPAM Startups & SMB provides top PHP developers to hire via team augmentation or dedicated development.

Featured PHP projects: although EPAM doesn’t advertise specific projects, they have experience all over the place, including partnerships with AWS, Adobe, Salesforce, and more.

Industry focus: none.

Iflexion

Years of experience: 15.

Location: Europe, with offices globally.

Pricing range: $25 – $49 / hour.

Who they are: Iflexion is a software development company that serves companies from SMBs to global enterprises like Adidas, eBay, and Xerox. In total, they have over 1500 completed projects.

PHP frameworks: Laravel, Symfony, Zend, Yii, and CodeIgniter

PHP development services: Inflexion’s services include both simple website development and advanced domain-specific solutions. Along with custom PHP development, they do integration with third-party systems, maintenance, and optimization.

Featured PHP projects: Iflexion’s PHP portfolio ranges from content management systems and e-commerce platforms to social networking solutions and CRM systems. Still, the key PHP project is a ticketing system for a construction company.

Industry focus: ecommerce, elearning, security, construction. But they develop web portals for all sorts of companies, too.

Capital Numbers

Years of experience: 12.

Location: India.

Pricing range: <$25 / hour, starting at $15 per hour.

Who they are: Capital Numbers is an ISO 27001 and 9001 certified software development company. They have over 750 developers that help them provide on-demand services. The company won multiple awards as one of India’s most trusted brands.

PHP frameworks: Laravel, CodeIgniter, Symfony, and Zend. Along with major CMS like WordPress and Drupal

PHP development services: Capital Numbers provides all sorts of software services. From application and API development to consulting, migration, and tech support.

Featured PHP projects: they built a Fintech app for improving the credit scores of bank clients. Laravel was used for web app development.

Industry focus: none. This is a major software development company. While it helps you access India’s top developers, the company doesn’t specialize in specific industries.

Seasia Infotech

Years of experience: 24.

Location: India, with offices in the USA, Canada, Australia, and Ghana.

Pricing range: $25 – $49 / hour.

Who they are: Seasia Infotech is a custom software development company with over 700 employees and 500 senior developers.

PHP frameworks: Zend, Laravel, CodeIgniter, WordPress, Joomla, and CakePHP.

PHP development services: the company’s PHP focus is not on full-cycle development services. Although they do develop PHP web applications from scratch, the focus is on PHP web design, performance optimization of existing systems, and PHP code reviews.

Featured PHP projects: these include an ERP for a real estate company, DMV application for the US government, and an online uniform builder.

Industry focus: none. Seasia Infotech stands out as a company that can develop solutions for all industries, without specializations.

Rexsoft

Years of experience: 12.

Location: Ukraine, with offices in the USA.

Pricing range: $25 – $49 / hour.

Who they are: Rexoft is a small software development company dedicated to scooter sharing and logistics industries.

PHP frameworks: Laravel and WordPress CMS

PHP development services: the company provides full-cycle PHP development, of both smaller websites and major logistics apps. Apart from developing, they do consulting and maintenance of finished applications.

Featured PHP projects: the company is renowned for its scooter app development. They’ve worked on two major scooter rental apps. As for PHP projects, they’ve done CRM development for a logistics company, a ticket seller, and several security companies.

Industry focus: mainly, the company is focused on scooter mobile app development. But they also provide PHP web development in multiple different domains.

Zibtek

Years of experience: 15.

Location: Global, with headquarters in the United States.

Pricing range: $50-99 / hour.

Who they are: a custom software development company that provides IT staff augmentation services including, design, project management, web and mobile development, QA automation, dev ops, and support services. In total, the company has over 200 employees.

PHP frameworks: WordPress.

PHP development services: the company lets you hire PHP developers with experience building WordPress websites.

Featured PHP projects: Zibtek has around a thousand projects in its portfolio including one for Adobe and many for startups like Wantoo, Volley Metrics, or Statix.

Industry focus: none. The clients have all sorts of backgrounds, from marketing and finance to education and food deliveries.

Sapphire Software Solutions

Years of experience: 23.

Location: India, with offices globally: in the US, Canada, UAE, UK.

Pricing range: <$25 / hour.

Who they are: Sapphire Software Solutions is an Indian software company with 200 employees. They serve both major enterprises like L’oreal or Bayer, and smaller startups. This is a very experienced company with 1500 finished projects.

PHP frameworks: Laravel, YII, CodeIgniter, and Symfony. They work with WordPress and Drupal CSS, too.

PHP development services: they are a full-cycle company, meaning that they do it all. Starting with from scratch development, and finishing with migrations, updates, backend design, and API integrations.

Featured PHP projects: in terms of PHP, they mostly do websites. If you need a landing page, this is a great choice.

Industry focus: none. Most of Sapphire’s projects are small, so if you need a company that would go in-depth, look elsewhere.

Upqode

Years of experience: 9.

Location: USA.

Pricing range: $100 – $149 / hour.

Who they are: Upqode is a premium web design and marketing agency from Nashville, Tennessee.

PHP frameworks: WordPress.

PHP development services: this is a great choice when you need a nearshore development website building company. They’ll deliver a great website and help you market it. However, if you need a complex system, better check out other companies in the list.

Featured PHP projects: Upqode has developed over three hundred projects. Many are nominated for best design awards by Awwwards, Clutch, and CSS Design Awards.

Industry focus: ecommerce mostly, but really any company that needs web development and marketing.

BelitSoft

Years of experience: 15.

Location: Europe, with headquarters in the US.

Pricing range: $50 – $99 / hour.

Who they are: BelitSoft provides a range of enterprise technologies and services, with a focus on customized software solutions. They have 250 employees in staff, and the customers include Honeywell, Berkeley, and Technicolor.

PHP frameworks: Laravel, Yii2, Zend, Slim, Symfony.

PHP development services: the company provides PHP development services from scratch, staff augmentation, and migration to a newer PHP version. The full development cycle includes UI/UX design, consulting and business analysis, QA and testing, dedicated teams, and long-term maintenance.

Featured PHP projects: to list a couple, there are a corporate training LXP, a CRM database for a healthcare company, and a custom marketplace for the beauty industry.

Industry focus: though Belit soft works with dozens of industries, their focus is on education, finance, and healthcare.

Vention Teams

Years of experience: 20.

Location: headquarters in the United States, with offices in the United Kingdom and Poland.

Pricing range: $50 – $99 / hour.

Who they are: Vention is a leading software development company. They offer innovative solutions to both startups and large enterprises. Vention has over 3000 employees worldwide, which helps them align with the client’s time. They boast 500 award winning clients and $15 billion in client acquisitions.

PHP frameworks: Zend, Yii, CakePHP, Symfony, Laravel. They also work with major CMS like Shopify, WooCommerce, Magento, BigCommerce, WordPress, Drupal, and Joomla.

PHP development services: the company provides multi-functional engineering, backend development, porting and migration, as well as cloud and DevOps services. Finally, a big chunk is PHP team augmentation services.

Featured PHP projects: they’ve developed GovOS, a digital government platform that powered 2700 local and state government agencies. Other projects include Barchart, a fintech platform that powers leaders all over the world; and Freshley, a service of ready-meal deliveries.

Industry focus: the company overall has a greater focus on industries like Fintech, real estate, healthtech, ecommerce, edtech, marketing/adtech, and gamedev. But as far as PHP goes, there are many other industries covered.

Vardot

Years of experience: 13.

Location: headquarters in the US with an office in Jordan.

Pricing range: $100 – $149 / hour.

Who they are: Vardot develops custom open source CMS software. Most of their projects are in ecommerce, working with Drupal. The clients include the United Nations, Fitness First, and Vodafone. The company also has several products: an enterprise CMS distribution, a digital media platform builder, a knowledge base platform, a social business platform, and an open marketing platform.

PHP frameworks: Drupal

PHP development services: Vardot is focused almost exclusively on Drupal, so they are great at it. In fact, they’ve received numerous awards as best Drupal developers.

Featured PHP projects: a donation platform for the United Nations Refugee Agency that has collected $11.5 million. They’ve also developed platforms for multiple other UN projects, numerous universities, ministries, and central banks.

Industry focus: mostly non-commercial organizations in education, media, and healthcare. But really, the company has an impressive portfolio with clients from all walks of life.

Instinctools

Years of experience: 24.

Location: global with headquarters in Germany and offices and development hubs in Poland, Canada, the United States, Latin America, and Kazakhstan.

Pricing range: $25 – $49 / hour.

Who they are: Instinctools is a software product development and consulting company. They have over 350 experts and 650 completed projects. Over two thirds of developers are middle and senior tier.

PHP frameworks: most popular frameworks and CMS platforms.

PHP development services: Instinctools lets you hire PHP developers for consulting for all sorts of projects: consulting, API development, full-cycle development, maintenance, version upgrades, and application optimization.

Featured PHP projects: the company has 11 projects on their portfolio page. From promo websites to web applications, and a staff management platform.

Industry focus: no specific industry, but the list includes technology, entertainment and media, manufacturing, ecommerce, education, healthcare, fintech, logistics, automotive, cryptocurrencies, ad-tech, oil and gas, biotech, agtech, and energy.

Scalo Soft

Years of experience: 17.

Location: Poland, with an office in the US.

Pricing range: $50 – $99 / hour.

Who they are: Scalo is a software development company that partners with forward-thinking organizations. They have 750 delivered projects and 600 developers currently on board.

PHP frameworks: Symfony, YII framework, Cake PHP.

PHP development services: Scalo Soft develops custom web and desktop applications as well as provides dedicated development teams. The development covers all stages from analysis and design to development, QA, and maintenance.

Featured projects: they’ve done multiple staff augmentation projects, but most are under NDA. Their clients are many major Polish banks, like ING, Santander, and BNP.

Industry focus: fintech & banking, e-commerce, manufacturing, logistics, media & entertainment, and SaaS platforms.

PolCode

Years of experience: 18.

Location: Poland.

Pricing range: $50 – $99 / hour.

Who they are: PolCode Information is a web development company. The team has 150 experts that have delivered 1400 projects. Much of the work happens via Upwork, where PolCode is a leading PHP development company.

PHP frameworks: Laravel and Symfony.

PHP development services: Polcode is focused on smaller projects, mostly website development. However, they do provide enterprise grade solutions just as well. On top of development, the company does migrations, upgrades, rescue of legacy applications, support, and maintenance.

Featured PHP projects: online mentoring platform to an eLearning company from the UK. Another solution is an appointment booking platform for a fitness client in the Netherlands.

Industry focus: the major industries include retail, fintech, healthcare, manufacturing, and logistics. Since they mostly do smaller applications, the company is basically industry independent.

Sigma Software Group

Years of experience: 22.

Location: headquarters in Ukraine, offices in Sweden, the US, Poland, UAE, and Israel.

Pricing range: $50 – $99 / hour.

Who they are: Sigma Software Group is a major development company with over 2000 employees worldwide. Sigma is one of the world’s top 100 largest outsourcing companies. They are ISO 9001 and 27001 certified and are Microsoft’s Gold Partner.

PHP frameworks: Laravel, CodeIgniter, YII, Symfony, and Zend.

PHP development services: the company focuses on software development, not outsourcing. So in terms of PHP, they are a great choice when you’re building or need consultations on a major project.

Featured PHP projects: one would be a healthcare patent recruitment system. Another is a long-term 13-year software development for a tech company.

Industry focus: no specific focus, but they’ve had projects in multiple industries: aviation, construction, gambling, telecommunications, media, finance and banking, information technology, healthcare, energy, logistics, and even blockchain.

BitCot

Years of experience: 13.

Location: California, USA.

Pricing range: $25-$49 / hour.

Who they are: BitCot is a full service application development company that’s mainly focused on AI implementation. This is a pretty small agency with 40 employees.

PHP frameworks: Cake PHP, Laravel, Symfony, Yii 2, and CodeIgniter. As well as CMS platforms WordPress, Joomla, and Drupal.

PHP development services: as BitCot is a small agency, they don’t do staff augmentation. Instead they can build an application from scratch or modernize an existing application. The do both web application development and PHP-based CMS development.

Featured PHP projects: they’ve developed a Laravel-based CMS where clients can learn about the company’s services and get in touch with the team. In another project, they’ve developed an investigation management platform with CodeIgniter.

Industry focus: the main industries are manufacturing, fitness, fintech, healthcare, non-profit, and ecommerce / retail. Yet the company will work with clients in other domains too.

Aalpha Information

Years of experience: 16.

Location: India, with offices in the United States and Norway.

Pricing range: < $25 / hour.

Who they are: Aalpha does web & mobile application development for companies of all sizes, from SMB to enterprises, like Securitas, Swiss Re, and the World Bank. In total, Aalpha has finished over 5000 projects.

PHP frameworks: Laravel and Symfony.

PHP development services: they mostly do staff augmentation. Apart from that, they do migrations, API integrations, and development from scratch. It’s a great company to hire Laravel developers if you have a smaller budget.

Featured PHP projects: Aalpha developed a podcast management system with Laravel. After the development, the client has seen a 30% increase in efficiency.

Industry focus: none. This is a good company for clients in non-regulated industries. But if you need something like a major HIPAA compliant app, it’s better to check out the more expensive options.

Five Jars

Years of experience: 7.

Location: headquarters in the US, offices in Ukraine and Poland.

Pricing range: $100 – $149 / hour.

Who they are: Five Jars is a global digital transformation agency working with PHP. While still young, the company was already 40 times nominated as the best web developers. They’ve worked on a hundred projects that serve in total 30 million users.

PHP frameworks: Symfony, Laravel, and all the most popular CMSs.

PHP development services: apart from full-cycle open-source development, the company also does API design, accessibility works, and continuous integration workflows.

Featured PHP projects: there are two major PHP projects. One was upgrading and modernisation of Mount Wernon’s website with 2 million monthly visitors – the new site could triple the sales tickets. Another project is a migration from a legacy CMS to Drupal 9 for the University of Rochesterʼs Warner School of Education

Industry focus: education, media, health, and nonprofit organizations.

RadixWeb

Years of experience: 24.

Location: India.

Pricing range: $25 – $49 / hour.

Who they are: RadixWeb is a team augmentation company. They work with both startups and Fortune 500 companies to upgrade legacy applications and develop powerful web apps from scratch.

PHP frameworks: Laravel, Yii, CodeIgniter, Zend, CakePHP.

PHP development services: RadixWeb does migration of legacy applications and development from scratch. They can optimize your legacy applications and perform code reviews.

Featured PHP projects: whether it’s crafting intuitive CRM solutions, robust CMS development, or scalable e-commerce solutions, they will do the job.

Industry focus: none. Radixweb has a rich portfolio of PHP projects. They sign [projects regardless of the industry, spanning across healthcare, eLearning, LegalTech, Fintech, Manufacturing, Retail & Ecommerce, Travel & Hospitality, and Industry 4.0.

Plavno

Years of experience: 16.

Location: Czech Republic, with offices in Kazakhstan, Poland, and the United Kingdom.

Pricing range: $25 – $49 / hour.

Who they are: Plavno is a software company with a hundred developers on board. They provide top 3% of developers to customers that need industry expert teams.

PHP frameworks: Laravel and Symfony. They work with the main CSS as well: WordPress, Drupal CSS and Joomla.

PHP development services: while they provide all sorts of development, in terms of PHP the focus is on web portals, social networks, and custom CMS solutions.

Featured PHP projects: Plavno does major projects, often with artificial intelligence. Some examples would be an event business platform or a truck fleet manager.

Industry focus: healthcare, AI, blockchain, FinTech, EdTech, and e-commerce. They have teams of developers dedicated to the specific industry. This helps them stand out as a great choice for companies that build difficult projects.

Toptal

Years of experience: 14.

Location: global.

Pricing range: $100 – $149 / hour.

Who they are: Toptal is a platform to hire freelance developers, so it’s somewhat different from other companies in this list. The marketplace approves only the best developers, so you can be sure that all the staff is well qualified.

PHP frameworks: Any possible framework, since this is a marketplace.

PHP development services: you’ll be able to hire PHP developers fast and add them to your team within 48 hours. One advantage is that you can scale up and down quickly.

Featured PHP projects: Toptal has over 20000 clients, including Duolingo, Heinz, Bridgestone, and more.

Industry focus: one downside of TopTal is that it’s harder to build an industry specific team.

Andersen

Years of experience: 17.

Location: global, with development centers in Europe and the Caucasus.

Pricing range: $50 – $99 / hour.

Who they are: Andersen is a software development company with 3500 employees. It’s one of the biggest team augmentation companies in Europe. With Andersen, you can kick off a project in as fast as 10-15 days.

PHP frameworks: Laravel, Symfony, and ReactPHP. They also work with the major CMS: WordPress, Drupal, and Magento.

PHP development services: Andersen has over 50 PHP developers. They provide API development services, PHP web development, code audits, and system automation services.

Featured PHP projects: They have 50 PHP projects.

Industry focus: Andersen’s industries are financial services, media & entertainment, healthcare, telecom, logistics and automotive. But in terms of PHP development, their main focus is on ecommerce.

Human Made

Years of experience: 14.

Location: United Kingdom.

Pricing range: $150 – $199 / hour.

Who they are: Human Made is a WordPress agency for enterprise customers. This means that they they take WordPress to the limit so they could deliver software to companies like NewsUK, USA Today, Capgemini, and Unison.

Frameworks & CMS: WordPress.

PHP development services: they only work with WordPress, which means that their backend developers are true PHP experts. Apart from software from scratch, they do migrations, upgrades, API, and even AI development.

Featured PHP projects: Human Made helped Google on a global project to democratize publishing and improve the web. Some other partners include giants like NASA, Siemens, and USA Today. This is the company you go to if you want the absolute best WordPress solution.

Industry focus: major enterprises.

HTMLPanda

Years of experience: 10.

Location: India, with offices in the United States.

Pricing range: < $25 / hour.

Who they are: HTMLPanda is a company that develops websites. Their standout feature is design-to-website. You can sketch a website in PSD, and HTMLPanda will transform it into a live website.

CMS platforms: WordPress, Drupal, WooCommerce, Shopify, Magento, Opencart.

PHP development services: this is a website development company. They’ll make you a website, enable DevOps, and will take care of all the maintenance. Note, that HTML Panda only works with simpler projects. If you need a complex web application, it’s better to choose another company.

Featured PHP projects: there are no major projects, but you’ll find dozens of simple websites in their portfolio with mobile friendly responsive design.

Industry focus: none. The company democratizes website development. Because the websites that they make are cheap, there will be no complexity or compliance with specific regulations.

KrishaWeb

Years of experience: 16.

Location: India.

Pricing range: $25 – $49 / hour.

Who they are: KrishaWeb is a web development company, mostly serving small and medium businesses, but also working with enterprises. Overall, it’s a company with 110 employees that has served over 1950 clients, including Toshiba, Truecom, and Nanoheal.

PHP frameworks: on top of working with the major frameworks, the company also develops custom PHP frameworks.

PHP development services: KrishaWeb does all sorts of PHP development. They do migration and upgrades, they develop custom APIs, they optimize performance, they let you hire PHP developers. And, of course, they build custom applications from scratch.

Featured PHP projects: these include custom enterprise web portals, CMS development projects, and hundreds of ecommerce solutions.

Industry focus: none. KrishaWeb does great software, but they try to be as affordable as possible. This means the company caters to any client but the employees don’t have too much industry depth.

What Can Go Wrong When Choosing PHP Developers?

PHP is an easy language to learn. It’s true, but this is a double-edged sword – because PHP is hard to master.

As a result, there is a ton of terrible PHP code. It may even work fine at first glance, but bad code is tough to maintain and improve.

So here’s what we suggest: hire good PHP developers. At Aristek, 88% of our developers and middle and senior-tier, most hold computer science degrees. That’s why we build great web applications. If you need PHP developers for your project in education, healthcare, logistics, or any other industry – get in touch for a free consultation. We’ll help you build the right project.

Generative AI in Education. Everything You Need to Know

Aristek Systems — Wed, 06 Mar 2024 10:59:48 +0000

This article explains generative AI in Education. The first part is a crash course in generative AI, machine learning, and deep learning. The second part talks about how to apply generative AI in Education. Finally, we talk about the challenges in Gen AI.

The article will help you talk to your colleagues about generative AI and make informed decisions. Let’s get started.

What Is Generative AI? Let’s Learn the Basics First

Generative AI can produce new content like text, audio, images, or a mix of that. But you probably know that already.

To get deeper, we need to start with the basics. When you understand the core concepts, it is much easier to grasp the big picture without getting boxed up. A combination of Generative AI with other AI solutions can bring amazing results.

AI is like an onion, with many layers inside – Gen AI is in the very middle. Let’s peel that onion from the top.

Artificial Intelligence (AI) is a technology that helps machines mimic human intelligence. AI systems can see, learn, solve problems, or make decisions.

Machine Learning (ML) is a subset of AI that relies on adaptive learning. Machine learning behavior is not explicitly programmed, the algorithms learn autonomously. That’s why such models can get better over time and not stay rigid.

By contrast, non-ML AI models are rigid and rule-based. Most modern AI systems apply at least some elements of machine learning. That’s why the terms are often used interchangeably. But you can still find non-ML AI models in fields like robotics, speech, or image recognition.

Deep learning (DL) is a subset of ML that uses neural networks. A neural network is a set of different formulas that mimic how our brains work.

To train the model, developers upload real-world input and output data. The AI will try to find connections between input and output. They are called the hidden layers. At first, the hidden layers are random and can be incorrect. But after each step of training, the connections become more accurate.

Let’s say, you want to create a neural network that predicts exam grades. First, you need a random hypothesis. Does perfect attendance equal perfect test scores? The model tests it and compares the results to real world data. If the results were not correct, the neural network will work the math magic to come up with better predictors of test scores. This process will take many iterations, until the developers are satisfied with the accuracy.

The more diverse your input data, the more accurate the results. To estimate exam scores, you may need to collect more than just attendance. It could be student grade history or time spent on finishing homework.

Generative AI. Finally, Gen AIis a subset of Deep Learning. Deep Learning can be of 2 types: discriminative and generative.

Generative AI generates new data, while Discriminative DL is used to classify and predict data.

Here’s how to tell if you have generative AI or not. It is Gen AI when the output is content like text, audio, image, video, or any combination of those. It’s not generative if the output is mathematical: a probability, a number, or a classification (is a student cheating or not?).

Want to learn more? Read our in-depth guide to Machine Learning.

Different Types of Generative AI Content

Generative AI goes beyond ChatGPT and Midjourney. Various models can take different inputs to produce a wide range of outputs. But let’s focus on the most popular input types: text and images.

Image Input

Image-to-text can add captions to images. This is how your phone can sort out your photos and tell your friends apart from your pets, or your documents. Thanks to these models, you can search images or ask questions about them.
Image-to-image models can improve image resolution. If you have a blurry or damaged picture, such models can fix it.
Image-to-video models can animate your pictures.

Text Input

Text-to-text models can translate or summarize texts, correct your grammatical mistakes, or answer questions. LLMs like ChatGPT combine all the functions and generate all sorts of text.
Text-to-image models generate images and videos from your text prompts.
Text-to-audio can simulate sounds from a written text. There are even text-to-song models that generate music with a certain mood or specific chords.

Combinations

Several Gen AI, mixed. There are plenty of Gen AI types. Once you start combining the algorithms, the list becomes exponentially longer.

For example, let’s take a voice assistant that can generate a video lecture. First, it needs to converse your voice prompt to text. Then, another algorithm will generate a lesson structure based on your request. A third model creates illustrations and puts them together in a video. Finally, a text-to-speech algorithm will read the lesson. That’s 4 different models put together.

Gen AI + other AI. Once you start combining generative AI with other AI solutions, the long list becomes endless.

Back to our lecture generation. What if we add a model that analyzes the performance of every student and adjust the lecture accordingly? To struggling students, the model will thoroughly explain the basics with more examples. To outliers, the model will get deeper and explain advanced material.

How to Apply Generative AI in Education

Teacher shortage is real all around the world. By 2030, Europe and North America will need to hire a third of teacher numbers from 2022. Subsaharan Africa with its booming population will need to almost triple the teacher numbers. Here’s how generative AI can save the day.

24/7 tutors. With Gen AI, personalized learning won’t be for the privileged few. Millions of students will be able to study customized materials and ask questions to their AI tutor.

Faster lesson creation. Overworked teachers will spend less time creating lesson plans, rubrics, and assessments. AI can generate a draft in a second, while still keeping curriculum requirements in mind.

Internationalization. Generative AI can not only translate content but also adapt it to fit the local context, replacing cultural references and idioms.

Better accessibility. AI can adjust content to specifically tailor a student’s disability. For example, visually impaired learners will benefit from text-to-speech and image recognition. While learners with hearing disabilities will take advantage of speech-to-text.

Adaptive summaries. Many students no longer take notes in online learning. Automatic summaries can help. To make sure that students still pay attention in class and don’t just rely on summaries, AI solutions can add engagement recognition. If students are engaged, they get personalized summaries. If not, they’ll get a basic recap.

Content creation. AI can generate additional learning materials in seconds. This can be simple content like flashcards and discussion questions. But it can also be 3D models, illustrations, or animations.

Adaptive learning paths. We know that each student is unique. Some learn quickly, some need time to progress. Gen AI can adapt to each student’s progress, so no learner is bored or feels left behind.

Adaptive testing. The best tests are unique to every learner, not standardized. When a platform knows the student’s track record, it can maintain the right level of challenge. Tests can specifically target the weakest points or mix in easier questions to reinforce the memory.

Not just Gen AI. AI is much more than generating content. Read our AI in e-learning guide to learn more about advanced analytics, improved learning paths, and more.

Best Gen AI Solutions Are Personalized

Without personalized data, generational AI can only go so far. Best solutions don’t just generate content. They generate specific content for specific learners. Thankfully, there’s a standard protocol designed for tracking user experiences.

xAPI (Tin Can) is the industry-standard protocol for tracking learning experiences. If you want to analyze learner actions, you’ll need xAPI. With it, you can track any user actions:

Which parts of a lecture do learners skip?
What grades do they get?
On which quiz questions do students spend the most time?
What actions and decisions do learners make in games?

The protocol is multi-platform, supporting mobile, desktop, web, and even wearable devices. This means that xAPI can collect data from all learning software.

If you want to learn more, read our in-depth guide to xAPI.

How to Integrate AI With School’s Software?

Let’s say you have an AI tool. How do you get it to users?

K-12 companies should be the first to worry about integrations. Schools already have a digital ecosystem, and they don’t want kids to remember more passwords. So your AI tool must fit in with the school LMS. You can develop a great solution, only to find out that the school doesn’t support it.

How to fix the issue? Make sure, your software complies with industry protocols: OneRoster and LTI.

OneRoster is a set of standard rules for organizing student data. Thanks to OneRoster, different LMS and SIS can synchronize.

LTI is a standard for synchronizing LMS with all other learning software, including your AI tool.

What’s the Next Step?

Read our article on machine learning for deeper tech understanding. Read about OneRoster and LTI if you’re in K-12 and higher education. Read about xAPI if you want to collect student experience data.

Need help? Book a free consultation with our Generative AI experts. We build AI solutions for corporate education, curriculum publishers, and eLearning startups. On the call, we’ll suggest how Gen AI can step up your company. We’ll also answer any questions you may have.

How to Boost Corporate Training with an Enterprise Class LMS

Aristek Systems — Tue, 05 Mar 2024 11:33:33 +0000

Any business is, first of all, a team of workers.

That’s why business development should be aimed at honing employees’ skills and knowledge.

The success formula is simple: the better your team, the more efficient your business is.

The best way to combine a professional education and a favorable working atmosphere in the team is to provide corporate training.

A major research project by Middlesex University’s Institute for Work Based Learning found that 74% of UK workers felt they were not fulfilling their potential and wished they had received more career training.

No wonder that the Chartered Institute of Personnel and Development found that 95% of hiring managers considered corporate training a key retention tool.

This is where learning management systems (LMS) will come in handy.

What Is a Corporate LMS?

The corporate LMS stands for the enterprise level learning management system.

This software allows you to administer distance learning training courses.

It is designed to meet the training needs of organizations by providing a centralized platform for managing and tracking employee learning and development.

Distance corporate training is more convenient for workers because it’s a remote learning method. It allows them to study anytime and anywhere.

It’s also easier for employers to prepare online courses using an LMS, as they eliminate the need for travel and other organizational issues.

What’s more, corporate LMS can automate all processes of training courses. So, there is an opportunity to minimize the number of staff necessary for training.

This means that there is no risk that the human factor can negatively affect the system. Also, it is easier to track problems and liquidate them.

Another great advantage of a corporate LMS is that it’s capable of satisfying all the needs of your workers.

For instance, orientation courses, vocational education, client support upskilling, familiarization with the qualities of the product or service, learning about regulatory compliance, and many more.

Why choose LMS for Corporate Training

The choice of LMS platform for corporate training has a number of benefits:

Approachability
The program ensures high-quality distance learning, which allows workers to study anytime, regardless of their current location. Moreover, they can use any device and operating system for distance courses.

Usability
LMS is an easy-to-use system whose interface and navigation are clear for students and teachers with different user experiences.

It offers a variety of features such as online quizzes, discussion boards, and grade tracking. Overall, LMS enhances the learning experience by providing a centralized platform for communication and resource sharing.

Automation
Due to the automatic control of all the processes, the risk of errors is reduced and the problem-solving procedure is accelerated.

Additionally, LMS allows for easy access to course materials and assignments, promoting organization and efficiency for both students and instructors. This streamlined approach to education fosters a more interactive and engaging learning environment.

Individualization
Learning management systems can adapt to every customer. So, it is possible to provide corporate training in accordance with the individual requirements of a certain business owner.

Furthermore, LMS can also offer personalized learning paths and assessments to cater to the specific needs of each learner. This flexibility allows for a more tailored and effective training experience, ultimately leading to improved outcomes for both the organization and its employees.

Security
It is very important that LMS protects the personal data of all users and assures security for financial information and payments as well.

By prioritizing data security and privacy, LMS can build trust with both businesses and employees, ensuring a safe and reliable learning environment. Additionally, regular updates and maintenance of the system are essential to keep up with evolving technology and industry standards.

Scalability
The elearning system is flexible and does not limit users. Therefore, it is possible to develop corporate training and implement new technologies into the system.

Analytics
You always have available dashboards where you can see the progress of your colleagues. At the same time, LMS can mark out those topics and sections where they need some individual coaching sessions in case of some omissions or errors.

Having an advanced learning management system will lead to increased employee engagement and motivation, as well as improved learning retention rates.

Additionally, a corporate LMS can streamline training processes and reduce overall costs for organizations.

Corporate LMS Features

Corporate training systems commonly have such features as learning management, testing, content authoring, extended enterprise, a course library, and certification and compliance management.

Custom LMS enables even more functionality, including multi-user system administration, user chat, maintenance for comments and recommendations from coaches, white labeling, video materials streaming.

You can also have communication in real time, lesson structuring mechanisms, electronic commerce modules, easy effectiveness tracking, and an AI assistant.

AI technology can make online learning personal. It analyzes learners’ behavior, tailors lessons to every student, provides 24/4 digital tutoring, and frees teachers’ time with automated grading.

This personalized approach can lead to improved student engagement and better academic outcomes.

Additionally, AI technology can also provide real-time feedback to students, helping them track their progress and make adjustments as needed.
If you’re interested in learning more about AI in eLearning, read this article.

Custom corporate LMS allows the construction of the optimum architecture of the enterprise-grade LMS, which is able to meet all main SCORM needs.

Such an approach to system building makes the enterprise-grade LMS compatible with a great number of eLearning services, and leads to the creation of the new monetization source.

In addition, the enhancement of the LMS app with white-label functionality and various subscription functions allows for increased revenue channels.

It’s also possible to evaluate the effectiveness of the corporate LMS because it can be synchronized with a complex information center module, which provides analysis of workers’ morale, teaching ROI, students’ performance, etc.

The elearning system gives a possibility to get a large number of employees to study in a short time without a lot of effort.

The automation of all processes is another benefit of the corporate LMS.

You do not need to control the training yourself because the LMS is a mechanism that detects errors and solves problems on its own.

This allows for a more efficient and streamlined training experience, saving time and resources for the organization.

What’s more, automation can help ensure consistency in training delivery across all employees.

Choosing The Best Option

Corporate LMS is a go-to option for businesses across different industries.

You can choose ready-made platforms, because they typically have all the main features. Yet, they aren’t tailored for specific requirements and your business needs.

Custom LMS gives you more flexibility, innovation, and independence but takes longer to build.

We created the Aristek eLearning Ecosystem to get the best of both worlds.

On the one hand, this is a completely ready platform. The LMS has a teacher & student suite, a content builder, analytics, and everything else an LMS needs.

On the other hand, we provide full customization. Any feature or integration you need, we can make it happen. We can even rebuild the ecosystem with a different tech stack. Our framework will help us develop fully custom EdTech software, but much faster.

Whether you need fully custom software or Aristek eLearning, Aristek Systems will be glad to help you.
We have plenty of successful projects under our belt and are happy to share our expertise. For more information, get in touch with us.

Breakdown of AI Compliance in 2024. Why Focus On Privacy?

Aristek Systems — Fri, 01 Mar 2024 10:56:15 +0000

AI can be dangerous. The stronger it becomes, the more people worry about AI compliance. There are good reasons for this. AI solutions go through enormous datasets; they can misuse private data, cause discrimination, or simply generate false predictions.

That’s why we hear much about AI regulation: the EU AI Act or the AI Bill of Rights in the US. But they are not enforced yet. Instead, we have many local and industry-specific laws that regulate AI.

For now, companies need to focus on privacy regulations. AI solutions complicate compliance with GDPR, ISO 27001, and others. If you’re dealing with clients from the EU, US, or China, be on guard.

This article lays out major regulations for AI compliance. Let’s dive in.

Note: this isn’t legal advice. Use this article for general understanding, but do consult your legal advisors before building an AI system.

The EU AI Act is Coming. Why It Will Be Important Worldwide

So far no country has a detailed legal framework for AI compliance. The regulations are still in the Wild West. Done wrong, AI can unintentionally mess up grades, recruitment, road safety, justice, and the list goes on. But this should soon change, starting with the EU.

EU Artificial Intelligence Act. It is likely to become law sometime in 2024. Like GDPR, the EU AI law will affect the entire world, including the US. If your AI will have users in the EU, you’ll have to comply.

The act is focused on high-risk AI applications, protecting people from the worst side effects of AI.

Here’s how it works. It puts all AI into 4 risk categories:

Unacceptable risk solutions are outright banned. These include social scoring systems, biometric classification, and facial recognition in public places. Yet there can be some exceptions for law enforcement.
High-risk solutions will be regularly assessed by the EU regulators. This includes products that require EU product safety: toys, medical devices, cars, aviation, and lifts. But it also applies to critical industries, like education, employment, law enforcement, or legal interpretation.
General purpose and generative AI will have to be transparent and safe. Advanced models like GPT-4 will be audited. But even simple generative AIs should disclose that the content is AI-generated, and it should never generate illegal content.
Limited risk models will have minimal transparency requirements, but won’t have to be evaluated. For example, such solutions will have to disclose when users interact with an AI.

Fines for non-compliance can be severe. They’ll go up to €36 million or 7% of yearly turnover – whichever is higher.

Other countries will be looking at the EU Act. Meanwhile, they are rolling out regulations, too:

Canada’s Artificial Intelligence and Data Act;
China’s Administrative Measures for Generative Artificial Intelligence Services (Generative AI Measures);
Brazil’s Bill 21/20;
Several other countries in the Americas and Asia are also developing their regulations.

The US Doesn’t Have Major AI Laws

In the USA; AI compliance is not straightforward. So far, only local and state laws regulate AI here. But in a few years, federal AI laws might come out too. Here’s the rundown.

Executive Order 14110 is the most extensive AI regulation so far.

It doesn’t have immediate consequences for most AI developers. The only requirements are for new major AI models that start posing risks to national securities. Such developers have to notify the federal government when training the systems and share the results of the safety tests. But this doesn’t apply to most AI models.

Instead, the Executive Act instructs government agencies to develop new rules soon. It also requires some federal agencies to appoint Chief AI Officers.

Blueprint for an AI Bill of Rights is a purely voluntary document. Yet, future regulations may rely on it. The blueprint has 5 main principles:

Safe and Effective Systems.It would protect users from unsafe systems by pre-deployment testing, risk identification, and ongoing monitoring;
Algorithmic Discrimination Protections. It protects users against discrimination, based on race, gender, age, or any other sensitive characteristics;
Data Privacy. It requires AI systems to have built-in privacy protections, and users should have agency over how their private data is used;
Notice and Explanation. Systems should notify users when they use AI and how it would impact them.
Human Alternatives, Consideration, and Fallback. Whenever appropriate, users should be able to opt out of the AI tool and communicate with a person instead.

The Algorithmic Accountability Act has been introduced, but it’s far from becoming law. If passed, it would require companies to do 3 major things:

Assess the impacts of the AI systems;
Be transparent about using AI;
Empower consumers to make informed choices when they interact with AI systems.

The act applies to critical decisions in healthcare, housing, education, employment, and personal life.

Locally, US Regulations Already Require AI Compliance

While general AI regulations are still yet to come, we already have laws that govern specific AI applications. Usually, these are state or local laws, here are some of them:

NYC Law 144 prohibits biased automatic recruitment tools. Before using automated employment decision tools, any company has to pass an audit proving that such tools are unbiased.

Colorado SB21-169 prohibits insurance companies from discriminating against customers. With Big Data, insurance companies could unintentionally harm protected groups. The new law protects algorithm discrimination based on race, color, national or ethnic origin, religion, sex, sexual orientation, disability, or gender.

Connecticut SB 1103 regulates the state government’s use of AI. Since early 2024 all state agencies will have to inventory AI systems that they use. Furthermore, they need to perform assessments to make sure that their AIs don’t discriminate or provide unfair advantage to anyone.

Why You Should Focus on Privacy & GDPR

Until we have major AI regulations, companies should focus on data protection and privacy. These laws are not new, but AI makes it harder to comply than regular software.

Since there is no US federal data protection regulation, the main focus is on GDPR. But we’ll cover Californian, Chinese, and Canadian laws too.

Should the US companies care about GDPR? Yes. GDPR applies to any business in the world if it has users from the EU. That’s why even most US companies need to follow it.

However, there are local GDPR equivalents in the US and all over the world: CCPA in California, PIPEDA in Canada, PIPL in China, etc. That’s why websites all over the world ask for your cookie permission.

What is GDPR? The name is pretty straightforward: General Data Protection Regulation. Perhaps, the world’s strongest one.

It protects any data that can identify a person, directly or indirectly. You can identify a person by their name and location (i.e. John Johnson is near the Eiffel Tower). But you can track someone even without a name. Police can identify a car owner by the license plates. Tech companies can track users cross-site with 3rd party cookies. This could also be info about health, race, or political beliefs.

Penalties for non-compliance are huge. Smaller offenses result in fines up to €10 million or 2% of global turnover, whichever is greater. Major offenses can be fined up to €20 million or 4% of global turnover, whichever is higher. In 2023, Meta got the highest-ever GDPR fines, twice: €390 million and €1.2 billion.

What are the 7 key principles of GDPR? All principles are equally important for GDPR compliance. Some come from the 1995 Data Protection Directive, others are new.

Lawfulness, fairness, and transparency. You have to identify legal reasons for data processing. You should be transparent about what you’re doing with the personal data.
Purpose limitation. You must only collect personal data for explicit and legitimate purposes. In other words, you can’t claim to never sell cookies to 3rd parties, and then… sell cookies to third parties.
Data minimization. Only collect data that is necessary for your purpose, never just in case.
Accuracy. Keep personal data accurate and up-to-date. Let your users correct their data or erase it.
Storage limitation. Store personal data only for as long as necessary. Establish clear timeframes and dispose of the data when it’s no longer needed. Some retention periods are specified in law, like on tax records. In other cases, they can be more fluid – but you need to set an appropriate time frame.
Security (Integrity and confidentiality). Implement appropriate security measures: encrypt your data, isolate PII from other data, etc. Learn more in our article about personally identifiable information.
Accountability. The controller (we’ll describe who it is soon) is responsible for compliance with the other 6 principles. Be prepared to demonstrate compliance to supervisory authorities upon request.

What are GDPR controllers and the processors? Under GDPR companies that handle personal data are either controllers or processors.

The status is important because controllers have way more obligations than processors. If there’s a breach on the processor’s end, the controller is still responsible for dealing with it. The status affects your relationship with suppliers and customers.

Controllers determine why and how to process data. In formal terms, they define the purpose and the essential means of processing. Some of the essential means include:

What data to process?
What’s the retention period?
Should they disclose the data?

Processors process the data on behalf of the controller. They follow the controller’s instructions and can’t appoint sub-processors without the controller’s consent. They can define the non-essential means, like:

Programming languages, frameworks;
Security measures;
Architectural measures: APIs, microservices, and virtual machines.

What does GDPR mean for AI compliance? The important part is to determine whether your AI vendor will be a controller or a processor.

Companies working with GDPR are used to granting the processor role to software vendors. However, AI vendors will often need the controller role.

Let’s break down some examples:

How to Handle Privacy in Other Countries?

Here’s the trick: make your privacy governance principle-based. If you base your privacy on principles, it’ll be much easier to adjust to many specific regulations.

In most countries, regional regulations are in some way similar to GDPR. There are differences, but they still have a lot in common. For example, while GDPR describes Controllers and Processors, California’s CCPR has similar roles named Businesses and Service Providers.

To give you an idea, here are some privacy regulations around the globe:

In the USA, there is no federal data protection regulation. Instead, states pass legislation individually. There are California’s CCPA, Colorado’s CPA, New York’s NYPA, and so on.

A federal privacy act called ADPPA has been introduced, but not yet approved.

In China, there’s PIPL (Personal Information Protection Law). For foreign companies, the most important part is cross-border data controls. There are strict rules on transferring private data of out China.

Under PIPL, there are three mechanisms for exporting personal information out of the PRC:

Security Assessment;
Certification;
Standard Contract.

Yet, the original requirements lacked clarity and were difficult to achieve overall.

In 2023, China eased cross-border data controls in Draft Provisions. Now, there’s a new threshold for security assessment. If a company transfers data of under a million individuals, there’s no need for a security assessment. Here’s the new threshold:

> 1 million individuals. Requires security assessment;
10,000-1 million. Requires security certification or entering a standard contract published by Cyberspace Administration of China (CAC);
< 10,000 individuals. No required transfer mechanism.

Still, all important data transfers require a security assessment. At first, the definition of “important data” was murky. Now, companies can presume that they don’t possess important data unless they were informed otherwise by regulators or through a public notice.

Other countries have their own rules. In Canada, there’s PIPEDA. In Brazil, there’s LGPD. In Japan, there’s APPI. Be aware of them before entering a local market.

Information Security: SOC 2 and ISO 27001

These standards protect sensitive information. They both control processes, policies, and technologies in an organization.

The standards have their differences. ISO is international, while SOC 2 is only popular in North America. SOC 2 applies only to service companies, while ISO 27001 can apply to any organization.

Neither SOC 2 nor ISO 27001 are legally required. Yet many customers demand compliance from their software vendors.

SOC 2 is a security compliance report designed for companies that handle customer data, be it cloud computing or machine learning development.

As a customer, you don’t have to go too deep, but check if your vendor complies with SOC 2. The report revolves around 5 Trust Service Criteria, but only security is obligatory.

Security. Ensuring protection against unauthorized access and data breaches. Again, this criterion is required for all SOC 2 reports;
Availability. Ensuring that applications are accessible and operational;
Processing Integrity. Verifying that AI processes are accurate, complete, and timely;
Confidentiality. Safeguarding sensitive data from unauthorized disclosure;
Privacy. Managing personal information in accordance with privacy policies and regulations.

SOC reports come in into two types:

Type I is short-form. It assesses how organizations handle control at a specific point;
Type II is long form and can cover over a year. The actual audit usually takes only a few weeks but covers the entire period.

ISO 27001 is another standard for information security. It checks compliance of your Information Security Management System (ISMS). This means, your software vendor will need to establish an ISMS.

The goal of an ISMS is to protect three aspects of data. Unlike SOC 2, ISO 27001 requires compliance with all 3:

Confidentiality. Only authorized persons have the right to access information;
Integrity. Only authorized persons can change the information;
Availability. The information must be accessible to authorized persons whenever it is needed.

US Industry-specific Regulations

Critical industries have their regulations, too. Here are just a few of the US industry-specific laws:

In Healthcare, HIPAA is the most important regulation. It makes sure that your health information is kept private and secure. It sets rules for doctors, hospitals, and others about how they can use and share your health details.

HIPAA also gives you the right to control your health information and limits how your information can be shared without your permission.

In Education, the most important laws for AI developers are FERPA and COPPA. Read more in our article about privacy in education.

FERPA limits how public entities can access educational information and records.

COPPA is a privacy law. It focuses on online services that collect personal information from children below 13 years old.

In Finance, GLBA is a crucial regulation. It has guidelines for banks and financial institutions, outlining how they can use personal financial details.

GLBA also provides you with rights to control your financial information and places restrictions on how this information can be shared without your consent.

How to Handle AI Compliance in 2024

AI Regulation is coming, so companies better get prepared. For now, focus on the privacy protections like GDPR or CCPA.

Here are the key steps for handling AI compliance:

Establish a clear system of compliance policies. With so many laws, it’s easy to get lost – so keep things organized. Understand what markets will you be in and single out their local privacy and AI regulations;
Map your compliance. Compliance mapping software helps you keep track of regulations, both old and incoming ones. Getting such a solution will simplify compliance;
Hire a vendor that takes compliance seriously. For example, at Aristek Systems we develop AI, ML, and Data Science solutions with a heavy focus on security and compliance. Want to learn more? Reach out for a free consultation.

Top-Rated Corporate LMS in 2024

Aristek Systems — Thu, 29 Feb 2024 16:20:57 +0000

They say it’s never too late to learn.

We say it’s never too late to help your employees learn.

Here’s why:

Canadian association Go2HR found out that around 40% of employees who do not receive enough training end up leaving their post within a year.

Let’s translate this into money.

Oxford Economics puts the cost of brain drain at around $30,000.

Meanwhile, the average expenditure of $1,500 per employee for ongoing training barely registers in comparison.

What about tangible benefits?

A tech giant, Motorola calculated that for every dollar spent on staff training programs, they gained 30% in productivity. And that was just within three years.

This is where the learning management system (LMS) comes in.

Where to Start?

Before you start choosing a corporate LMS, there’s something you should know.

There are many different types of LMS for various needs and requirements.

First, decide whether you need an off-the-shelf product or a custom solution before delving deeper into the nuances.

They both have pros and cons. Check out our SWOT Analysis to learn more.

If you ask us, we support custom solutions. Here’s why:

Personalization = Retention

By keeping users engaged, customization boosts retention rates.

When content is personalized and interactive, learners feel connected and more invested in their training.

What’s more, personalization goes beyond just content. It extends to the navigation, user interface, and assessment tools.

Smooth Integration with Existing Systems

Customization allows for integration with existing systems.

By customizing a solution, you can ensure that it integrates smoothly with the existing systems utilized by the company.

Flexibility Is a Key

Development from scratch or customization allows you to have more control over the LMS and you can customize your platform at any moment.

Another big advantage is truly owning the LMS, as you own rights to the platform.

Top 10 Corporate LMS

Here are our picks for the best corporate learning management systems.

As business needs vary, their order is not of great importance.

Our goal is to help you find THAT LMS.

Aristek Custom LMS

Aristek Systems develops corporate LMS solutions with a personal touch.

Every company has a unique set of requirements based on its business industry, projects, organizational culture, and finances.

Aristek gets it and take everything into account.

Having 22+ years of LMS development experience, certified quality, and plenty of successful LMS projects under it’s belt Aristek works with companies of all sizes across different industries.

Partners rely on it’s technical skills and commitment to rapidly scale quality products, transform UX/ UI overnight, and tackle complex integrations.

To ensure the best LMS experience, Aristek adheres to elearning tech standards.

For example, with xAPI you can track even real-world experience, get advanced analytics and create better courses.

Aristek’s solutions don’t just meet needs; they catalyze transformation.

You can upgrade your LMS with AI and provide better education through personalized and adaptive learning paths, automated grading and scheduling, predictive analytics, and AI assistants.

Towuti LMS

The next learning management system on our list is an off-the-shelf solution — Towuti LMS.

Through the use of its virtual classroom, this corporate LMS facilitates blended learning and allows for the organization of synchronous training sessions, group discussions, or one-on-one tutoring.

Learners also get access to a personalized site that shows their certificates, checklists, assigned courses, and forthcoming training sessions.

Additionally, its themes, templates, and content collection help create learning content quickly.

Docebo LMS

Docebo is a cloud-based LMS that companies can use to administrate, deliver, and track training programs.

It has advanced features such as microlearning, gamification, social learning, and mobile learning.

Its innovative features don’t stop there.

Docebo also uses automated course content recommendations, which allow users to discover new information and develop professionally.

As for managers and admins, it provides tracking of learning metrics and progress with reports and dashboards.

Talent LMS

TalentLMS is a good choice for small businesses as it’s easy to install.

It offers a user-friendly interface and a variety of features to create and deliver engaging online courses for employees.

Its features include live sessions, tests and quizzes, learning paths, completion rules, video conferences, reports, and third-party integrations.

Moreover, this LMS supports 30 languages and has its own library for soft skills training.

Looop LMS

Looop is an advanced learning management system that automates employee training processes and creates online resources.

Its user-friendly interface makes it easy for both administrators and learners to navigate and use its tools effectively.

Furthermore, it offers a visually appealing UX, campaigns, and content delivery through channels like Slack, Teams, and SMS.

It also provides user insights and analytics dashboards for performance assessment.

Continu

“Continu is a learning management system built for today’s workplace.”

Continu offers an elegant user interface that facilitates access to necessary training for staff members, clients, and outside suppliers.

What’s more, its extensive integrations with various technologies make it simple to import or write material directly within a platform by using writing tools.

As a result, less time is spent on developing content.

Bridge

Bridge is a complex solution that integrates corporate LMS functionality with performance management and career development features.

It enables tailored assessments, self-enrollment in courses, and a categorized catalog of learning content.

In addition, its features include a profound system of enrollment and completion tracking and automated course notifications.

Workramp

WorkRamp is an LMS for training that functions as a comprehensive corporate digital learning platform for your staff, particularly those in sales, support, retail, marketing, and HR.

Its biggest advantage is that it allows the creation of personalized learning paths for employees according to their roles, teams, abilities, or seniority.

That’s not all. It offers enhanced training material production, dashboards and analytics to measure learning progress, and a dedicated onboarding software approach.

Cornerstone Ondemand

Another well-liked LMS is Cornerstone Ondemand, because it provides each employee with a personalized learning experience.

The platform offers thorough statistics and reporting on skill development, teams, and individual learning.

Multiple languages can be used to distribute content to people throughout the globe.

Additionally, applications like Adobe Connect, Microsoft Teams, Webex, and Zoom may be integrated with this solution.

Adobe Learning Manager

Learning Manager allows you to manage training effectively across employees.
The primary users are LMS Managers & Admins, Corporate Trainers, CX Professionals, Sales

Professionals, HR Leaders, Marketing Leaders, Legal & Compliance teams etc.

The LMS is pretty innovative. For example, machine learning (ML) and artificial intelligence (AI) tailor learning journeys with relevant recommendations.

It has also automated user onboarding and course assignments.

The Choice is Yours

Simple as it is.

There are plenty of worthwhile ready-made solutions on the market that offer various functionalities and features. Choose what suits your business best.

If you want to have more flexibility, independence, and innovation, or just something of your own, we’re there for you.

AI in Healthcare: A Closer Look

Aristek Systems — Tue, 20 Feb 2024 13:34:33 +0000

For the best possible insights, we’ve combed through various scientific studies, examined plenty of statistics, and talked to experts in the field.

Hold on tight. This will be a captivating read.

AI in Healthcare: Theory & Practice

For the time being, there are numerous uses of AI and ML in the healthcare industry.

To get a deeper understanding of AI technology, read this comprehensive guide first.

Yet, six in ten Americans say they would be uncomfortable if their healthcare provider relied on AI to treat them.

Clearly, questions arise.

Is there anything to worry about, at least for the moment?
Can AI be truly helpful in treatment?
And how is it actually used today?

We’ll assess AI efficiency in healthcare using two criteria:

Theory: research findings, AI prospects, and potential;
Practice: real-world use cases, tangible benefits, and current challenges.

Diagnosis & Treatment

Today, many tech companies are developing algorithms for interpreting images and big data prediction models.

Their mission is to create “a co-pilot” for clinicians to assist with diagnosis and treatment decisions.

Theory

Researchers are now claiming that their AI-based approach can diagnose and treat diseases as accurately as a human clinician. Or even better.

Studies have shown AI’s ability to meet or exceed human experts’ performance in image-based diagnoses in radiology, dermatology, pathology, and cardiology.

For example, a study published in the UK used an AI system to diagnose breast cancer based on a large dataset of mammograms.

It showed that an AI-based mammogram interpretation system decreased false positive and negative results, which will potentially improve accuracy of breast cancer screening.

Another research used artificial intelligence to detect skin cancer.

It showed that AI technology had a 100% detection rate for melanoma and was 99.5% accurate in detecting all skin cancers.

Providers and payers for care also use ‘population health’ ML models to predict populations at risk of particular diseases or accidents.

These models can be effective, but they sometimes lack relevant data that adds predictive capability.

For instance, patient socio-economic status.

Practice

Indeed, automated classification of medical images proves to be useful in diagnostics and treatment.

In fact, it is the leading AI application today.

A recent review of AI/ ML-based medical devices approved in the USA and Europe from 2015 to 2020 found that 58% of devices in the USA and 53% in Europe were approved or CE marked for radiological use.

Let’s see how it works:

Imagine a computer tomography (CT) scan.

AI can find a very similar scan and tell the dynamics of changes based on these two pictures.

Now, instead of a few experts interpreting a scan, picture ten million radiologists doing so.

That’s basically how AI uses medical imaging to help reduce diagnostic mistakes by detecting anomalies a human might overlook.

What’s more, AI can learn from a wealth of data.

An AI tool that has learned all the ways a disease can present itself in an imaging study can quickly derive answers from new imaging studies.

And all this without getting tired.

A good example of a platform that uses AI is Nuance’s Precision Imaging Network. It interprets pictures and provides radiologists with recommendations.

We must say, recommendations is a keyword here.

Today, AI doesn’t produce an independent prognosis. No medical institutions fully delegate diagnostics to AI to avoid lawsuits.

Strictly speaking, it is used for more accurate measurements and highlighting spots potentially useful for the doctor.

There are more challenges to using AI in healthcare.

According to the scientific report, AI implementation issues are an uphill struggle for many healthcare organizations.

This situation is beginning to change, but AI is mostly present in research labs and in tech firms rather than in clinical practice.

Another challenge for AI adoption in healthcare is ensuring widespread use.

This requires regulatory approval, integration with EHR systems, standardization, clinician training, funding, and field updates.

Therefore, scientists claim that AI’s maturity will take longer, resulting in limited use within 5 years and more extensive use within 10.

Administrative Applications

AI can optimize administrative tasks in all kinds of industries. No wonder, this kind of application is popular.

Theory

According to McKinsey, GenAI represents a meaningful new tool that can help unlock a piece of the unrealized $1 trillion of improvement potential present in the industry.

“It can do so by automating tedious and error-prone operational work, bringing years of clinical data to a clinician’s fingertips in seconds, and by modernizing health systems infrastructure.”

These are needed in healthcare because, for example, the average US nurse spends 25% of work time on regulatory and administrative activities.

Practice

This is the case where theory actually corresponds to the practice.

Here is how AI is used to optimize administrative tasks.

RPA (Robotic Process Automation) platforms are used for revenue cycle management, clinical recording, claims processing, medical record administration, and continuity care.

For example, they summarize discharge information and follow-up needs for post-acute care.They can also generate care summaries for referrals, and synthesize specialist notes for the primary-care physician team.

Machine learning is used in claims and payment administration for probabilistic data matching across databases.

As insurers have a duty to verify whether the millions of claims are correct, ML helps identify and correct issues faster and more effectively.

As a result, it saves all stakeholders (health insurers, governments and providers) a great deal of time, money and effort.

NLP systems are primarily used for clinical documentation and research.

Speech and text recognition are increasingly utilized in healthcare for tasks like patient communication and clinical note capture.

Patient Engagement

One of the most promising but tricky AI uses in healthcare is patient engagement and communication.

Theory

At present, AI-based capabilities are being explored for personalized care, with a focus on machine learning and business rule engines for targeted interventions and timely action.

The main question is: can AI personalize and contextualize care?

Can it be achieved by using messaging alerts and targeted content that provoke actions at moments? That’s an intriguing field of research.

Another major focus is guiding patient behavior in a more anticipatory manner using real-world evidence. This is how it looks:

Software collects information from provider EHR systems, biosensors, wearables, cell phones, etc;
Then compares patient data to previous effective treatment routes for similar cohorts;
Finally, it adapts suggestions based on all of the above.

The advice might be given to doctors, patients, nurses, call center representatives, or care delivery coordinators.

This leads us to virtual health assistants.

Virtual assistant is an innovative technology designed to simulate human conversation and offer personalized patient care based on input from the patient.

According to research, virtual assistants use AI-powered applications, chatbots, sounds, and interfaces.

They help patients to identify the problem based on the symptoms, provide medical advice, remind patients to take their medications, schedule doctor appointments, and monitor vital signs.

Practice

AI chatbots (e.g. Babylon and Ada) can identify symptoms and recommend further actions in community and primary care settings.

They can also be integrated with wearable devices like smartwatches to help patients and caregivers improve their behavior, sleep, and overall well-being.

For example, Emerald is a wireless sensor and ML platform for remote monitoring of sleep, breathing, and behavior.

It was founded by Massachusetts Institute of Technology faculty and researchers.

Yet, let’s remember that AI chatbots don’t provide you with a diagnosis.

Their suggestions only have a recommendation character, which they constantly remind us of.

Some healthcare organizations experiment with chatbots for patient interaction, mental health and wellness, and telehealth.

However, in a survey of 500 US users of the top five chatbots used in healthcare, patients expressed concern about revealing confidential information, discussing complex health conditions, and poor usability.

Surgeries

This is a sphere where AI has proven to be highly effective.
For this purpose, we won’t even examine theory and practice separately.
First surgical robots were approved in the USA in 2000.

This technology provides ‘superpowers’ to surgeons, improving their ability to see and create precise and minimally invasive incisions.

Common procedures performed using robots include gynecologic, prostate, head, and neck surgeries.

Ryan Cameron, vice president of technology and innovation at Children’s Hospital & Medical Center in Nebraska says:

“We do a high volume of laparoscopic surgeries using the da Vinci Surgical System. It’s hard for a person to achieve perfect stillness because of breathing and heart rate. The da Vinci system absorbs the surgeon’s natural body movements and makes sure everything happens with great precision.”

However, important decisions are still made by human surgeons.

Some more great uses of AI include 3D organ reconstruction for surgeons to plan operations and computer vision during surgeries to evaluate surgery control.

Drug Discovery & New Therapies

Whether AI can find new treatments is one of the most puzzling questions.

Let’s see what the current situation is.

Theory

AI is expected to drive significant improvement in clinical trial design and optimization of drug manufacturing processes.

The recent announcements by DeepMind and AlphaFold pave the way for better understanding disease processes, predicting protein structures, and developing more targeted therapeutics.

AI tools on multimodal datasets could enhance disease understanding and clustering, as well as patient population clustering.

It will enable targeted preventive strategies like immunomics for diagnosis and treatment prediction.

This will be revolutionary for multiple standards of care, with particular impact in the cancer, neurological, and rare disease spaces. As a result, personalizing the experience of care for the individual.

Practice

Over the past decade, synthetic biology has produced developments like CRISPR gene editing and some personalized cancer therapies.

Yet, the creation process for such sophisticated treatments is still incredibly costly and inefficient.

In the future, AI will enable us to handle far more systematic complexity and, consequently, help us transform the way we understand, discover, and affect biology.

This will be made possible by improved access to various data.

The efficiency of the drug discovery process is expected to improve by helping better predict early which agents are more likely to be effective, and anticipate adverse drug effects.

That is a critical point because these issues have stymied the development of effective drugs at a costly late stage in the development process.

As a result, we can expect more democratic access to advanced therapies at a lower cost.

Key Points

AI healthcare research is accelerating rapidly. Potential use cases in healthcare include drug discovery, virtual assistants, disease diagnosis and treatment, health monitoring, and many others.

Currently, AI systems can’t reason the same way as human physicians. Instead, AI resembles a signal translator, translating patterns from datasets.

AI systems today are beginning to be adopted by healthcare organizations to automate time consuming, high volume repetitive tasks. They can also be used only to help doctors during surgeries or assist with diagnostics.

Yet, they don’t diagnose independently. The same works for AI apps whose predictions have only a recommendation character.

This is the current situation with AI in healthcare. But forecasts are very promising. More extensive use is expected in the next ten years.

Polaris Market Research predicts AI in the healthcare market will be worth $431 billion by 2032.

If you are considering boosting your business with AI, now is the perfect time.

How to Put AI to Use in Business. 7 Essential Steps

Aristek Systems — Thu, 25 Jan 2024 09:00:41 +0000

We’re not going to extol the virtues of artificial intelligence. You can hear them from every direction. Let’s get straight to the practical side of the issue.

How Can AI Help My Business?

In this article, we’ll use the term ‘AI’ to refer to all the technologies that make up the field. If you would like to learn more about them, check out this guide first.

Let’s see what AI can do for your company.

Take Care of Tedious Tasks

Let’s be honest, not many employees fancy doing administrative tasks. It takes plenty of time, energy, and other resources. Yet, they are necessary.

Using artificial intelligence is a win-win for both people and businesses.

For example, RPA (Robotic Process Automation) platforms can automate tasks like scheduling, data entry, report generation, and other assignments for you.

They also provide real-time monitoring, data synchronization, and email notifications.

Don’t worry, it’s not quantity over quality. You can have both, as AI improves task accuracy by learning from data patterns.

As a result, your employees’ creativity and overall effectiveness will be increased.

According to Deloitte, digitally mature enterprises see a 4.3% ROI for their artificial intelligence projects in just 1.2 years after launch.

Improve Customer Experience

AI is a powerful tool for creating personalized offers. This is because of its algorithms.

AI assistants can analyze and forecast customer behavior, which helps businesses make smarter decisions.

Also, you’ve probably seen chatbots and virtual assistants that respond to website visitors instantly.

Chatbot technology is often used for common or frequently asked questions. Yet, companies can also implement AI to answer specific inquiries regarding their products, services, etc.

In general, having an AI assistant that works 24/7 saves customers’ time and improves their overall experience.

Assist in Analytics

AI is fully capable of producing sales forecasts and efficient predictive analytics.

It can analyze market tendencies, competitors’ strengths and weaknesses, and customer feedback.
Having an assistant that can work with a wealth of data ensures time-saving, in addition to better decision-making.

Handle Tasks That Programming Won’t

As AI goes beyond the limitations of traditional programming, it will help when old-school development is too tedious, costly, or unable to provide acceptable results.

Artificial intelligence allows businesses to deal with non-standard issues due to its flexibility.

What’s more, its uses are pretty creative. You can see them in a variety of industries.

For example, optimization of warehouses and equipment maintenance in logistics, creating a speech correction assistant, music play automated evaluation, and many more.

It can make your innovative idea a reality, if you give it a try.

Steps to Implement AI in Your Business

There is nothing wrong with keeping up with trends. Yet, progress solely for the sake of progress seems a poor business strategy.
To integrate AI into business efficiently, we recommend following these simple steps.

Step 1. Pinpoint Your Specific Business Needs & Goals

Take a closer look at your current business processes. What areas work well, could use improvement, or leave much to be desired?

Which process is a stumbling block for your employees? What can be done to improve customer service?

Which process takes a lot of time, money, and other resources?

A great tool to use for assessment is Force Field Analysis.

Basically, you should oppose forces that are driving change (e.g., a better customer experience) to restraining ones (e.g., high costs).

It’s essential to evaluate not only AI capabilities and limitations but also your internal readiness for tech adoption.

You can also use templates for assessments. Translating pros and cons into numbers may help you see the situation more clearly.

Step 2. Choose the AI Solution & Provider

Once you evaluate your business needs and budget, it’s much easier to pick the best AI solution.

Research available AI tools, and explore their flexibility, scalability, level of customization, and integration.

Analyze potential vendors’ expertise, reputation, and experience.

Examine regulatory compliance and security measures, as well as support offerings.

Step 3. Prepare the Data

AI systems’ success relies on high-quality data for training. To ensure this, gather more data or improve existing sources.

To complete this step, an experienced AI provider is often required. A team of experts will use techniques like data cleaning and preprocessing to ensure accuracy and spot potential issues.

As far as the business side is concerned, you only have to gather data and provide annotations to your vendors (often optional).

Following this step will maximize the effectiveness of your AI solution and improve business outcomes.

Step 4. Develop an Implementation Strategy

Like any other implementation project, AI adoption requires planning.

Set deadlines, milestones, and specific requirements. Find out potential threats, challenges, and data privacy regulations.

Assign responsibilities to team members (data scientists, ML engineers, etc) and discuss everything with them.

If your team lacks some necessary experience, consider partnering with vendors, ordering artificial intelligence consulting services, or outsourcing external talents.

Step 5. Ensure the Smooth Integration

After selecting the best AI solution and gathering data, your model will be trained to identify trends and provide accurate predictions.

Collaborate with data scientists and AI specialists for dependable results.

The AI model will be integrated into your company’s operations after training and testing it.

To complete it efficiently, your existing systems and procedures might require adjustments.

Therefore, a smooth transition to AI operations is impossible without interaction with important stakeholders and other necessary assistance.

Step 6. Set Guidelines & Protocols

A comprehensive data security and privacy policy, defining the scope of AI applications, and assessing judgments are crucial to maximizing AI’s benefits and reducing its risks.

Review and update these rules regularly, ensuring compliance with emerging technology and business requirements.

Step 7. Assess AI Performance

After implementing AI in your company, you should continuously check on its performance.

This is to make sure it operates well and produces the desired results.

To assess the effect of AI on your company, set up KPIs that correspond with your goals. For example, cost savings, better customer service, or enhanced business growth.

Analyze the data on a regular basis and identify problems and possible areas for development.

Conclusion

Implementing AI in business has incredible potential, but success requires careful strategy and execution. Moreover, AI models should be continuously enhanced and improved to gain a competitive advantage.

Hopefully, this guide will be helpful along the way. Moreover, our team of experts can make it a walk in the park for you. Feel free to get in touch.

Your Guide 2024 to Machine Learning. Finally Get It

Aristek Systems — Mon, 22 Jan 2024 13:24:46 +0000

Machine learning used to be for geeks and sci-fi fans. Now, ChatGPT alone has 180 million users. AI and machine learning tools spread fast, so it’s time to cover the basics.

This article is a crash course into machine learning for non-tech people. You can finally understand what ML is; how these models work; and how your company can benefit from it?

What Is Machine Learning?

Machine learning (ML) is a branch of artificial intelligence (AI) that relies on adaptive learning. All machine learning is AI, but not all AI is ML.

Machine learning behavior is not explicitly programmed, the algorithms learn autonomously. That’s why such models can get better over time and stay flexible.

By contrast, non-ML AI algorithms are rigid and rule based. When a computer can’t execute a line of code, it freezes not knowing what to do. If operating systems could handle unconventional situations with ML, we wouldn’t have “blue screens of death”.

Most modern AI systems apply at least some elements of machine learning development. That’s why the terms are often used interchangeably. But you can still find non ML AI models in fields like robotics, speech, or image recognition.

If you want to probe deeper and learn more about the connection of artificial intelligence, machine learning and deep learning, read this short guide first.

Types of Machine Learning Models & Algorithms

There are plenty of different algorithms, but we’ll focus on the high level types.

Supervised learning models learn by example. To train the model, developers feed it with extensive input and output data. As the AI identifies patterns, it can predict outcomes of new data.

There are two main types of supervised models:

Regression models find connections between variables. You can visualize such connections in a chart with X and Y axes, as in the picture above. If a coffee shop sells a cappuccino for $5, you can calculate the price of 10 cups. That’s called linear regression;
Classification models sort items into categories. Imagine a full fruit bowl. If I ask you to pick all the bananas out, you’ll instinctively look at the shape, color, and so on. To people, classification is simple and automatic. We don’t need to have seen a billion bananas to tell them apart. But computers need a ton of input, and that’s why we still have captchas (although bots have become better at it).

Supervised models are great when you have lots of labeled data. They are perfect for content recommendation systems, automated grading, and language learning tools.

Unsupervised learning doesn’t have human guidance. Developers feed AI with large datasets and let it find patterns on its own.

Clustering is a popular type of unsupervised learning. It’s similar to classification, except the data is unlabeled.

Let’s say I ask you to separate some gemstones by type. Even if you have never seen a gemstone, I bet you can tell rubies and emeralds apart. All because they look completely different: one’s reddish, the other is greenish.

Use unsupervised learning when you need to identify trends, organize content, or cluster students.

Reinforcement learning models are trained with feedback.

The algorithm interacts with the environment, and receives feedback – either rewards or penalties. The AI learns to maximize actions for long-term rewards.

Back to the gemstone example. Imagine that you need to pick all rubies out of 10 stones, but you don’t know what it looks like. If you pick the right gem, you can keep it. If you make a wrong choice, you’ll get hit with a stick. That’s reinforcement learning.

Use reinforcement learning models when you have a dynamic environment. With such models, you can simulate physics experiments, create adaptive learning paths, or power your interactive learning games.

How to Build a ML Model (with a Fruit Example)

Let’s build a model that would tell apart apples and bananas. First, we explain the apple-banana model development, then we’ll add some more theory.

Step 0. Understand Your Problem & Goals

We’ll start our fruit picking ML model at the next stage. Here’s why someone would develop one: to teach you the fundamentals of ML.

Define your problem with numbers. If you know where your starting point is, you can track the progress.

Let’s say, you have a grocery store and your milk often expires on the shelf. Here’s what you can do:

Track current situation. How much milk do you stock and sell? What kind of milk do you have and what is its shelf life? Where do you put milk? And so on.
Compare your results with competitors. Do they have the same problem, or have they figured out a solution?
Convert business goals to specific KPI. Milk expiration is caused by multiple reasons. Once we understand the cause of the problem, we can come up with solutions. We could strive to improve milk sales by 15%, or improve inventory tracking, or suggest stocking sterilized milk instead of pasteurized.

Step 1. Collect Data

Apple-banana model. The fruits are different in many ways: in shape, color, texture, weight, and so on. But for our simple model, only 2 parameters should be enough: color and shape.

To measure color we’ll use a spectrometer; for measuring the shape we’ll get a ruler. This is what it’ll look like:

Below are the most common sources to start with. Note, that many models combine data from different sources.

Open-source datasets. You may be surprised at the quality of open source data. Major tech companies and even governments provide free data for your models. Here’s a list of some public data sources.
Web scraping. If you want to collect data from websites, you can do it by hand. Or get a web scraper and become much faster. Popular scrapers are Scrapy and ProWebScraper.
In-house data. With custom data you can build the most specific models. Sometimes it’s the only way to achieve good results, because public data has its limits.

Step 2. Prepare & Preprocess the Data

The apple-banana example. Once we have the data, it’s time to prepare it:

Put the data together.
Randomize the order. We don’t want the model to look at the previous fruit. It should locate bananas independent of the fruit order.
Visualize the data. This way, we’ll see the datapoint balance. If the training data has way more bananas, the model will also label almost everything as bananas in the real world.
Split the data into training and evaluation sets. We need to keep a separate evaluation set to make sure it works with new input, too. Not just with the training set.
There are way more data preparation types out there. Let’s give deeper.

This is the longest and most expensive stage. Data preparation can take up to 80% of the machine learning model development. Here’s why.

In machine learning, there’s a saying: garbage in, garbage out. Your model is only as good as the data it’s trained on.

There’s a famous example with Amazon’s recruiting AI. The model consistently rated women’s applications much lower than men’s. Why? Was the ML model sexist? No. The model was trained on resumes submitted in the past 10 years. Most applicants were men, that’s why the model preferred them. In the end, Amazon had to scrap the hiring tool.

Now that we know how important data preprocessing is, let’s figure out what it’s all about. Here are the steps in data preprocessing:

Data cleaning. Machine learning engineers correct errors and fill in missing data. They handle the missing values, and address outliers or any anomalies. Most popular data cleaning techniques are imputation, removal, and transformation.

Data cleaning solves 2 issues: missing or noisy data.

For missing data, often you’ll fill in the values. But if you have a very good dataset, it’s okay to ignore the tuples.

If your data is noisy, you’ll need to erase meaningless garbage. We won’t go into too much detail, but noisy data is handled in 3 ways: binning, regression, and clustering.

Data transformation means converting raw data into a structured and usable format. These are the main ways to transform data:

Normalization. Think of normalization like adjusting the volume on your music player. It’s a way to make sure all your data values play nicely together by putting them in a specific range, like turning the volume knob between 0 and 100.
Attribute Selection. Imagine you have a bunch of tools, and you want to pick only the most useful ones for a specific job. Attribute selection is like choosing the best tools from your toolbox and creating new, more focused tools to make your work easier.
Discretization. If you have a bunch of numbers and you want to simplify things, discretization is like rounding those numbers to make them easier to handle.
Concept Hierarchy Generation. Can sound scary, but it simply means converting details into broader categories. Like banana is also a fruit.

Data reduction means reducing the dataset while keeping the important information. Think of reading the book summary instead of the whole book.

Feature Selection. This involves choosing a subset of relevant features from the dataset, removing unnecessary or duplicate ones. Techniques like correlation analysis, mutual information, and principal component analysis (PCA) are commonly used.
Feature Extraction. Transforming data into a lower-dimensional space while retaining important information. This is beneficial for high-dimensional and complex datasets. Techniques include PCA, linear discriminant analysis (LDA), and non-negative matrix factorization (NMF).
Sampling. Selecting a subset of data points to reduce the dataset size while preserving essential information. Techniques such as random sampling, stratified sampling, and systematic sampling are employed.
Clustering. Grouping similar data points together into clusters, replacing them with a representative centroid. Methods include k-means, hierarchical clustering, and density-based clustering.
Compression. Reducing the dataset size for storage and transmission without losing vital information. Techniques like wavelet compression, JPEG compression, and gzip compression are commonly used.

Step 3. Train the Model

We’ve already discussed major types of machine learning models above. And training each model takes a bit different approach.

For our apple-banana AI, we can use a simple linear model. Here’s how the model would train itself:

Start by drawing a random line that would divide our dataset.
Check if the random guess was any good. Did the model guess which fruits were bananas, and which ones were apples?
Update the values to improve the regression line.
Iterate. Check the results, and update the values again.

Step 4: Evaluation

Once the model is trained, we should test it against data that has never been used for training. For that, we kept the evaluation set.

With evaluation, we’ll see if the model can work with the real world data.

Some of the things we look at during evaluation are accuracy, precision, recall, and F1 score.

Step 5: Parameter Tuning

Once the model shows good results, you can further improve it with tuning.

When we started preparing data for our model, we implicitly assumed some values:

How many times do we run through the training set during training?
How much does the line change after each iteration?

Now is the time to test these assumptions and try new values.

Data scientists can tweak these parameters forever, so it’s important to know when to stop. You decide when the model is accurate enough.

Step 6: Deploy

Now that the model is accurate, we can make it work with future outcomes. Just need to feed the model with real-world data.

Finally, our model can predict which of the fruits is apple. But machine learning models can do much more.

Instead of locating apples, another model would locate tumors. Let’s talk about other examples.

Machine Learning Common Business Usage

Every internet-user faces machine learning one way or another.

The most common examples are product or content recommendations.

Next time you search for a video on YouTube, or simply explore Google search engine, you know that ML algorithms are helping you choose.

Another example is virtual assistants such as Apple’s Siri, Amazon’s Alexa or Google assistant.

They all rely on automatic speech recognition (ASR), computer speech recognition, or speech-to-text, to translate human speech into a written format.

You can also see various chatbots on websites that help you navigate better and answer the most frequently asked questions.

Moreover, you’ve probably used Open AI’s GPT-3. It is a neural network trained on a whole host of English language articles available on the internet and is capable of producing answers in response to text prompts.

There are more industry-specific uses of ML that help businesses perform better.

Healthcare

ML helps healthcare organizations improve diagnostics, treatment, and patient experience through visual assistants, medical image analysis, and virtual nursing.

Medicine production companies use machine learning for drug discovery, in clinical trials and manufacturing.

Veterinary

It helps a lot by analyzing large data sets, making accurate predictions, and developing personalized treatment plans, resulting in improved outcomes and better overall pet care.

eLearning

ML can revolutionize education by personalizing learning experiences, improving student outcomes, and optimizing educational resources.

With machine learning, educators can analyze large amounts of data to identify patterns and trends. It allows them to tailor instruction to individual students’ needs and provide targeted interventions for struggling learners.

Additionally, machine learning can help automate administrative tasks such as grading and scheduling.

eCommerce & Retail

ML can be used to predict future sales to allow business to plan revenue, supplies, and many others.

It’s hard to deal with a huge amount of data, memory and computational time restrictions. Yet, ML is more than capable of tackling these issues.

Retailers also use computer vision for inventory management, and personalization.

In addition to recommendation systems to personalize advertising, pricing, and services, ML is used for fraud detection, and real-time consumer engagements.

Finance

Machine learning is used by financial services companies for risk assessment, algorithmic trading, customer service, and personalized banking.

What’s more, ML uses include credit card defense and anomaly detection for fraud protection.

Insurance

Recommendation engines are able to provide choices to customers based on both their needs and the experiences of other users with certain insurance products.

Processing claims and underwriting both benefit from machine learning.

Logistics & Supply Chain Management

Machine learning systems assist logistics firms in improving traffic management, warehouse and route optimization, passenger safety, and fleet productivity.

ML solutions can also include traffic monitoring, driver support, precise delay forecasts, and predictive maintenance.

Benefits & Challenges of ML

ML has a lot to offer in any industry and for any company.

What’s more, ML makes it possible to extract adverse information from articles much faster and more effectively.

Benefits

Here are some common benefits for companies implementing AI and machine learning in practice:

Enhanced employee productivity thanks to automation of task assignments or tedious tasks.
Time-saving. For example, ML Document search allows searching for answers in thousands of texts related to the user’s questions.
Cost-efficiency. ML allows you to save money on equipment maintenance by providing predictive monitoring and preventive maintenance.
Improved customer experience. For instance, virtual assistants and chatbots can deal with customer requests more quickly. What’s more, ML can send personalized offers using customer behavior data analytics.
Better production efficiency.Machine learning can optimize the process using demand forecasting and predictive modeling.
A modern solution for old and new challenges. When old-school development is too tedious and expensive, machine learning will help, as it goes beyond limitations of traditional programming.

Challenges

Despite the number of benefits, machine learning isn’t perfect. Let’s talk about challenges.

ML bias. If algorithms are trained on data sets that exclude certain populations or contain mistakes, it can produce world models that are inaccurate, or even discriminatory.

As with Amazon’s AI hiring tool, the bias may be unintended, but due to a poor data preprocessing.

Privacy. The complexity of machine learning algorithms is one of the primary issues facing the AI sector.

Large volumes of data are needed for algorithms to be trained and improved, which presents a privacy and security risk.

Furthermore, consumers and regulatory agencies are becoming increasingly concerned about how to ensure that personal information is safe and utilized in an ethical manner.

Alignment issues. Some experts fear that AI will get out of control and destroy all humanity. The worst part, we won’t even see it coming until it’s too late.

For instance, there’s a petition signed by many famous people to slow down the development of AI.

Machine Learning Trends

According to Grand View Research (GVR), the global market size for artificial intelligence is projected to expand from $136.6 billion in 2022 to a whopping $1.8 trillion in 2030.

Here’s what you can expect in the next few years out of machine learning.

Multimodal ML. Most current AI are good at doing one job: playing chess, writing text, or optimizing inventory. But this is changing. OpenAI’s GPT-4 leads the way by processing text, images, and sound simultaneously, mimicking human sensory abilities. Other models will follow soon.

Agentic AI. Current models need human input to react. But there’s a change coming from reactive to proactive models. These advanced AI agents act autonomously, setting goals without direct human intervention. Imagine environmental monitoring systems detecting early signs of a forest fire or financial agents dynamically managing investment portfolios in real time.

Open Source ML. The democratization of AI continues through the rise of open source models. GitHub’s data showcases a surge in developer engagement with generative AI projects. Open source AI not only reduces costs but also encourages transparency. Yet concerns about misuse still persist.

Retrieval-augmented generation. We can’t fully trust AI yet, the models often hallucinate. ChatGPT can make up a completely false answer and present it with full confidence.

But Retrieval-Augmented Generation (RAG) can help solve it. RAG improves accuracy by blending text generation with facts from external sources.

Customized enterprise generative models. While massive tools like Midjourney and ChatGPT dominate consumer attention, business applications lean towards smaller, customized models. These models, tailored for specific niches like healthcare and finance, offer improved privacy, security, and efficiency.

Need for machine learning talent. The need for AI and machine learning talent continues to surge. Here are the most wanted positions: MLOps, ML programming, data science, data analysis, and operations.

Shadow AI, or unauthorized use of AI within organizations. As machine learning becomes more accessible, shadow AI gets more threatening. Mainly, experts are concerned with data privacy breaches. Companies are going to introduce AI policies if they want to protect themselves and their customers..

A reality check for generative AI. When ChatGPT and Midjourney became mainstream, there was a lot of hype. People were excited and afraid. But we’re starting to understand the limitations of current models. Generative AI has issues with output quality, security, ethics, and even integration difficulties. Let’s stay optimistic, but realize that machine learning still has a long way to go.

ML ethics and security risks. Machine learning raises lots of ethical concerns. Some are down to earth, like deep fakes, ransomware, phishing attacks. Others warn of AI takeover. We’ll only have more of such worries in the coming years.

Evolving regulations. 2024 is pivotal for AI and ML regulation globally. The EU’s AI Act sets a precedent potentially influencing global standards – just like it did with GDPR.

Meanwhile, in the U.S. there’s no federal legislation so far. But 7 major big tech companies voluntarily committed to manage AI risks. These are: Amazon, Anthropic, Google, Inflection, Meta, Microsoft, and OpenAI.

How to Implement Machine Learning?

Machine learning is still an emerging technology. Right now, companies have a window of opportunity to implement machine learning before the competitors.

What’s more, it’s often cheaper than you’d think. Sure, training a deep learning algorithm from scratch takes a lot of work. But even today there are packaged AI solutions that require much less development time. With them, you can skip directly to stage 5 of the ML development

Want to introduce ML to automate your organization? Reach out for a free machine learning consultation. We’ll take a look at your company and suggest a roadmap for introducing machine learning to match your specific needs.

Top 10 Data Science Consulting Companies in 2024

Aristek Systems — Fri, 22 Dec 2023 15:30:52 +0000

Data science is here. Every year it only gets bigger. It’s clear that companies will need some form of data science to evolve.

Yet only few businesses understand how to leverage data science. It means, now is the perfect time to outrun competitors But how?

This is where data science consulting companies emerge. They find ways to introduce machine learning and AI. Want to know how your company will benefit from data science? Hire a consulting company. When you have a clear view of what to do, some consulting companies also provide developers.

Today we’ll discuss the best data science consulting companies on the market.

When Do You Need Data Science Consulting

When you have tons of data, but don’t know what to do with it. Most enterprises store tons of data about customers and production. Consultants make sense of it to implore operations and improve productivity.

Let’s say you track user actions with xAPI. Not all data is useful, a lot of it is irrelevant. So you need to cleanse. Once that’s done, it’s time to analyze the data: find patterns and trends in it.
This is where consulting companies will help. Here’s what they can do:

Find Insights. Data scientists will dive deep into your data, uncovering hidden patterns, trends, and correlations that may not be apparent on the surface. By understanding your business and customers, consultants can provide tailored recommendations for marketing, product development, and customer engagement.

Predictive Modeling. Using advanced techniques, they build models that predict future outcomes, helping you make proactive decisions and stay ahead of the curve.

Optimizing Operations. Consultants identify inefficiencies in your processes, suggesting data-driven improvements that can enhance productivity and save resources. If you don’t have much experience with data science, consultant companies will assist in the practical implementation. They’ll help integrate data science into your business operations seamlessly.

Risk Management. Have an idea but not sure if it’s good? Or want to mitigate risks in your operations? Backup with data to manage the risks. Data science consultants analyze historical data, providing a data-driven approach to handle potential challenges.

Top Data Science Consulting Companies

Aristek Systems
Location: Vilnius, Lithuania

Data science expertise: Aristek provides all around data science consulting and development. Solutions range from simple integrations with prepackaged services to developing custom AI projects.

Here are just a few things they do. In eLearning, they create personalized learning paths; develop predictive analytics for student performance; and create content recommendation systems. In logistics, Aristek optimizes supply chains to minimize delays and improve operations.

But their experience transfers to other industries as well. Data scientists analyze data – whether it’s customer behavior, manufacturing processes or employee productivity.

Data science consulting they provide: Aristek helps introduce data science. They will create a strategy to improve operations and save resources. The experts will check for weak spots in your operations, and suggest practical solutions to improve.

Industry focus in data science: Education, Logistics, Veterinary, Healthcare, eCommerce, AR/VR, Oil & Gas, and more.

Do they hire? Yes, head to the Careers page for details. Even when there’s no active search, we’re always on the lookout for talent.

Artefact
Location: Global, with headquarters in France

Data science expertise: Artefact is a data & AI company. On top of consulting, they do engineering, data analysis and marketing.

Artefact has 1000+ customers worldwide, including Nissan, Heineken and Loreal. The projects range from BI visualizations to business transformation.

Data science consulting they provide: Artefact provides end-to-end consulting expertise. Here’s what the consulting encompasses: data strategy, demand forecasting, data factory, call center AI, and data for private equity.

Industry focus in data science: No particular focus, they serve clients regardless of the industry.

Do they hire? They do. They have +1000 employees, so there are always lots of positions in data, engineering and marketing.

InData Labs
Location: Vilnius, Lithuania

Data science expertise: InData Labs is a custom data science development firm. They do all sorts of data science solutions: from big data and predictive analytics to NLP and computer vision. Most of their customers are in industries like marketing, ecommerce, and healthcare.

Some of their data science projects include a neural network that does women’s cycle prediction and a movie recommendation engine for smart TVs.

Data science consulting they provide: InData Labs consults customers at every stage of AI development. It can be both from the business and technology perspectives. But you can also get general consulting or QA sessions.

Industry focus in data science: marketing, ecommerce, healthcare, logistics, finance, banking, and automotive.

Do they hire? Yes, they’re a small company with around 50-100 employees on board. Yet, they’re always on a lookout for data engineers, data scientists, and developers.

Miquido
Location: Krakow, Poland

Data science expertise: Miquido is a full-cycle software development company. Data science is just a part of what they do, along with application design and development.

Still, they are good at business intelligence and big data solutions. They help with ETL, data mining, and exploratory data analysis.

Some of their projects include data collection for streaming services, and a fintech analytics tool.

Data science consulting they provide: They are good at analyzing your data. Miquido will help you find the right solution for your business case.

Industry focus in data science: healthcare, e-commerce, entertainment, and fintech.

Do they hire? They have all sorts of open positions in development, data science, and sales. They even have internships in Krakow. But they don’t always have data science jobs, because it’s not their primary focus. This is a sizable company with around 200 employees.

Mosaic
Location: Virginia, United States

Data science expertise: Mosaic is a data science consultancy. They develop all sorts of AI and ML solutions.

Some of their projects are manufacturing optimization, and ML development for an IoT company. They partner with major brands like Boeing, United Airlines, Exxon, etc.

Data science consulting they provide: They will look at your business processes and find ways to improve it with data science. The consulting can start at different stages from concept and data discovery to development and AI augmentation.

Industry focus in data science: Transportation, Utility, Consumer Products, Manufacturing, Healthcare, Biotech, Retail, Oil, Services, and Aviation.

Do they hire? They have under 100 employees, so it’s a relatively small company. They don’t often have junior positions, but you’ll generally find some senior openings.

ITRex
Location: Kyiv, Ukraine

Data science expertise: ITRex is a software development company with focus on high tech solutions: from data science and AI to blockchain and Internet of Things.

They are great at both big data and ML. Some of the projects include data management for a haircare brand and a business intelligence solution for a major retail company.

Data science consulting they provide: They consult companies in need of digital transformation. They’ll help you make sense of messy data and automate your operations.

Industry focus in data science: Biotech, Retail, Education, Transportation, and more.

Do they hire? Sure, you’ll find lots of engineering jobs in Eastern Europe and Poland. ITRex has over 300 engineers on board, so new positions are frequent.

RTS Labs
Location: Virginia, United States

Data science expertise: This is an AI consulting company. They develop custom software with AI / ML, and they specialize in content generation ML models.

Some of the projects are an NLP model for a telecom company and predictive maintenance in the energy sector.

Data science consulting they provide: RTS Labs will find a way to apply data science to automate operations and reduce costs.

Industry focus in data science: Customer service, fintech, energy.

Do they hire? Not actively. This is a small agency, so they don’t often have job offerings. If you’re a senior data scientist, you can still apply on their website.

Data ART
Location: Global, but mostly Eastern Europe

Data science expertise: Data ART is a custom software engineering company. Along with data science, they design and develop all sorts of solutions, from mobile apps to IoT.

Data science consulting they provide: Data strategy, data management capability consulting, data platform consulting, solution design, and BI consulting.

Industry focus in data science: fintech, healthcare, retail, and travel.

Do they hire? The company has plenty of engineering and data science jobs. Since this is a global company with 6000 employees, you’ll find positions from anywhere in the world.

Soft Formance
Location: Lviv, Ukraine

Data science expertise: Soft Formance is a small software development company. Data science consulting is just a part of what they do.

Data science consulting they provide: The company will help you become data-driven. They assist data mining, management, and visualization. With Soft Formance’s help you’ll make your employees more productive, and will make customer experience personal.

Industry focus in data science: Digital Marketing, healthcare, retail, education, business services, cleaning services, waste recycling, and green energy.

Do they hire? Although a small company, Soft Formance offers many jobs for developers.

Addepto
Location: Warsaw, Poland

Data science expertise: This is an AI solution provider. Adepto is fully focused on developing ML models, performing advanced analytics and consulting companies in need of digital transformation.

The projects they do are plentiful. They’ve implemented ChatGPT API for text summaries, developed an image quality detector, and made compliance analysis for retail.

Data science consulting they provide: AI, MLOps, and Big Data consulting. Basically, they provide the full range of data science consulting services.

Industry focus in data science: Manufacturing, retail, finance, logistics, aviation, and tech companies.

Do they hire? Yes, there are plenty of jobs for data specialists. Both junior and senior. Remote applicants are welcome, too.

Now What?

Hold onto this article for decision making time. When seeking vendors, refer back to these insights.

Also, here’s a surprise offer for your engagement.

We’ll give you a complimentary consultation. Not sure how data science can benefit your business? No matter what business you’re in, chances are your resources are being wasted. We’ll find ways to solve the problem – so reach out.

Top 10 AI/ML Conferences You Must Visit in 2024

Aristek Systems — Tue, 19 Dec 2023 11:23:09 +0000

Feeling enthusiastic about new technologies, trying to keep up with the latest trends or just looking for like-minded connections?

This article will point you in the right direction.

We’ve done research to share top upcoming AI and ML conferences with you. Enjoy reading and find a perfect match for your goals.

Best AI Conferences

The progress made in artificial intelligence has been remarkable.

This year, we have witnessed breakthroughs in natural language processing, computer vision, and autonomous systems.

No wonder, it has become a topic of great interest and concern among experts and the general public alike.

World Summit AI

Or as organizers claim, the only AI Summit in the world that matters.

We’re not just on the cusp of something extraordinary, we’re in it.

And that is undoubtedly true.

WSAI was launched in 2017 and now it is the largest AI summit globally.

Drawing world leaders and changemakers, it focuses on AI strategy development, applications, risks, benefits, and opportunities.

Its past speakers include Apple, Facebook, Amazon, Alibaba, Tencent, Google, Intel, NASA, Uber, IBM Watson and many more.

By the way, everyone can apply to be a speaker. Yet, keep in mind, it’s very competitive.
Thousands of speaking submissions are received annually, and there is a three-year waiting list.

However, if it’s not your aim, there are plenty of other opportunities.

You can become a partner, show your company to the world and make useful connections.

You can meet anybody there. Literally.

Investors, academics, the biggest tech companies representors, startups founders, data scientists, developers are coming to Amsterdam on the 9th of October, 2024.

Super AI

Another outstanding event is Super AI.

In June, Singapore’s Marina Bay Sands will host a gathering of industry leaders, heads of state, entrepreneurs, researchers, and curious minds to unveil transformative AI technologies.

A range of activities will include speeches by industry experts, panel discussions on AI advancements, live demos and networking sessions.

Become a partner or buy a ticket for the conference and explore what Super AI has to offer!

4th Annual MENA Conversational AI Summit 2024

4th MENA Conversational AI Summit promises to be the perfect event.

It will reunite industry leaders in conversational AI, chatbots, virtual assistants, contact center automation, voice technology, and conversation design.

The summit will also showcase MENA’s latest Conversational AI advancements, focusing on automation, customer service, machine learning, deep learning, and their potential business transformation.

It’s a great opportunity to learn from real-world case studies, business insights, and industry leaders’ lessons.

Explore the best approaches, resources, and techniques for maximizing AI’s potential!

2024 IEEE Conference on Artificial Intelligence

Another event held in Singapore is the 2024 IEEE Conference on Artificial Intelligence.

In June, Marina Bay Sands will transform into a vibrant meeting of bright minds aiming to shape the future of AI.

The topic of AI in various fields will be covered. Education, Healthcare, Life Science, Metaverse etc.

What’s more, important aspects, such as sustainability, safety, societal implications, will be discussed.

Come and take part!

Rise of AI Conference

In 2014, ten people talking about Singularity marked the beginning of the Rise of AI.

As the artificial intelligence business grows, Rise of AI is now Europe’s leading platform.

Their goal is to create a vibrant European environment and enable artificial intelligence to prolong and improve human life, benefiting all people.

It’s your chance not only to gain valuable insights but to become a part of an incredible community!

Top Machine Learning Events

Machine Learning (ML) is a subset of AI that relies on adaptive learning.

Machine learning behavior is not explicitly programmed, the algorithms learn autonomously. That’s why such models can get better over time and not stay rigid.

You may read an article on our blog, which will help you understand better.

Machine Learning Week Europe

Machine Learning Week Europe is a conference focused on predictive analytics, machine learning, data mining, and Generative AI in various sectors.

It offers a regional platform for data science professionals to share success stories and insights.

For example, it provides an opportunity to become a speaker. You can easily apply on the official website.

Take an opportunity to learn and share your expertise by attending the Conference in November 2024.

Data Innovation Summit 2024

This conference takes place in several locations – Singapore, Dubai and Melbourne and Stockholm.

Check the dates and choose the best option for yourself.

If you can’t go, it also gives an opportunity to visit it online!

The Data Innovation Summit will gather innovative minds, enterprise practitioners, technology providers, start-ups, and academics working in Applied Data Innovation, Data Science, Big Data, ML, Applied AI, Generative AI, Data Management, Data Engineering, Architecture, Databases, and IoT.

It’s an ideal place to see cutting-edge tools, participate in hands-on workshops and interactive panels.

As a great bonus, organizers prepared a networking program, to make sure you can meet with interesting people with ease.

ICML 2024

This is the Forty-first International Conference on Machine Learning, which will be held in Vienna, Austria.

It’s a well-scheduled event with plenty of conference sessions, tutorials, and workshops.

For the time being, it’s planned as a physical conference, but some streaming elements will also be provided.

In addition, you can easily become an exhibitor there and present your innovations. Applications will soon be available.

Stay tuned and don’t miss any deadlines!

4th World Tech Summit on Big Data, Data Science and Machine Learning

The summit will be organized in London on the 28th of November. And will take 3 days altogether.

It will be especially of use for those interested in career development, students, and young researchers.

This is because it’s a great place to meet with tech giants, make connections and share your knowledge.

Join in and let the world know about your research or innovation!

Machine Learning Prague 2024

Last but not least, one of the biggest European conferences about ML, AI and Deep Learning applications will be running in person in Prague.

But you can also visit it online!

The conference will take three days, filled with interactive content, practical talks and useful workshops.

Everything is so well-organized, that you can gain a lot of benefits and make the most of such a short period of time.

Buy a ticket and have a great time in Prague!

How Can I Learn about AI & ML Now?

While waiting for the conferences, you can use another opportunity to improve your company’s productivity.

Talk to our experts and learn how AI development services and consulting will help your business by automating tedious tasks, analyzing data more efficiently, and making predictions with unparalleled accuracy.

Boost Your LMS Selection with an Effective RFP

Aristek Systems — Mon, 18 Dec 2023 10:04:43 +0000

Summary

In this article, we’ll cover everything you need to know about an LMS RFP:
what it is (just in case), reasons to use it, benefits, and sound tips for crafting it to achieve your goals.

What’s an LMS RFP?

Request for Proposal (RFP) has been a buzzword in the sphere of business.

Basically, it’s a document used by organizations looking for various solutions.

Thus, organizations use an LMS RFP(Learning Management System Request for Proposal) to find the right LMS for their needs.

The LMS RFP serves as a formal invitation for potential vendors to submit their proposals.

It mentions all the necessary technical requirements, criteria, LMS features and goals to be achieved.

Why Use an LMS RFP?

Here are some reasons to consider an LMS RFP.

Tool to Find the Best Value for Money

It’s always easier and faster to find the right LMS, if you know exactly what you are looking for.

By using LMS RFP, you can accurately and concisely put into writing:

what you need and expect LMS to do;
which business goals you plan to achieve;
which features are necessary, which are nice-to-haves, and which you can comfortably disregard.
The LMS RFP’s transparency will ensure that you pay a reasonable price and only for what you need.

As a result, you’ll get a balance between your budget and requirements.

What’s more, after picking the solution, you can always evaluate its efficiency by turning to LMS RFP again.

Tool to Save Time & Effort

In fact, LMS RFP is a win-win, for both vendors and seeking organizations.

LMS RFP informs vendors about important issues, such as budget, deadlines, the necessity to employ a certain technology before they start presentation.

Therefore, you save time and effort on communicating, analyzing, and considering options that won’t really help you.

LMS vendors will also be thankful for clear descriptions as it saves their time and reduces efforts in a similar way.

What’s more, they can also analyze various LMS RFPs to understand their customers’ needs better.

This will help them tailor their products and services accordingly and provide high-quality solutions on the market.

Tool to Manage Risks Effectively

By providing a comprehensive overview of the solution, including technical specifications, timelines, and cost estimates, you can avoid misunderstandings and reduce risk.

Both parties will align their expectations and ensure a successful collaboration.

Additionally, this level of detail allows for effective project planning and resource allocation, resulting in timely delivery and customer satisfaction.

How to Craft an LMS RFP?

These are the key elements and tips for crafting a comprehensive LMS RFP.

Introduce Yourself

Introduce your company to potential vendors, short and sweet. Describe what you do (industry), your objectives, recent technology stack and the purpose of LMS RFP.

If you used any platforms before, it would also be helpful to share your experience.

Describe the Project

Give an overview of the LMS project’s objectives, implementation schedule, and financial restrictions.

It’s also important to mention the number of potential users, their roles (Publisher, Learner, Administrator etc), courses you already have and materials you want to upload.

Provide Tech Requirements

Mention all the necessary features, scenarios of using them, functionality
and other specific requirements, such as integrations and compatibility.

Ask questions about content and course management, reporting formats and assessment tools.

List the applications, software, and services you intend to use with an e-learning platform.

Let’s say you purchased an LMS only to discover later that it is unable to read the structure of your online courses. Thus, determine whether any integrations are necessary or whether you can live without them.

However, if you ever get into this unpleasant situation, don’t be too upset. We have a solution for you.

eLearning Integration Tool is supported by highly experienced EdTech solution architects, engineers, cloud engineers, implementation support, data scientists (AI/ ML), and advisory services.

Specify Vendor Qualifications

Clarify how your ideal vendor looks like. Include industry experience, post-implementation customer support capabilities, training options, short-term and long-term costs.

Feel free to ask for case studies, clients testimonials, awards and other references.

Request a Sandbox Access

A sandbox is an environment designed to mimic the appearance and functionality of a fully developed system. If such an option is available, you should totally go for it.

Although it won’t have every function accessible, it will provide administrators and users an opportunity to explore the platform, find potential problems and familiarize themselves with the user interface.

Include Submission Guidelines

Potential vendors will be grateful for this information. Therefore, you’ll avoid typical questions from them and save your time.

Provide a required format for their submissions (structure), criteria for selection, process of evaluation, deadlines and contact info.

Mention Terms and Conditions

Provide all the necessary legal information, such as company policies on data security and privacy, and requirements related to compliance.

Additionally, it is crucial to highlight any industry-specific regulations or standards that the company adheres to in order to maintain compliance and safeguard sensitive information.

Wrapping Up

A well-written LMS RFP points your business or organization in the direction of the best Learning Management System.

Through precise specification, well-structured request for proposals, vendor assessment, and in-depth product trials, you may choose with assurance the ideal LMS platform that fits your business’s or organization’s particular requirements and objectives.

At Aristek Systems, we offer custom eLearning solutions and consulting. Contact our eLearning experts and get all your questions answered.