DEV Community: Svitla Systems Inc.

No Servers, Just Vibes… AWS Serverless Music Chart App

Svitla Systems Inc. — Thu, 19 Feb 2026 21:01:54 +0000

In Jonathan Swift’s Gulliver’s Travels, giant Gulliver was defeated by tiny Lilliputians. Even though they were small, due to their planning and synchronized activities, they successfully defeated the giant. This is a perfect metaphor for modern software construction. Developing an application was like being Gulliver, as a lot had to be managed in terms of complex, weighty infrastructure, e.g., server provisioning, planning for capacity, and keeping everything up and running.

But today, with serverless architecture, the playing field has changed. Like the Lilliputians, tiny, thin scripts can be incredibly powerful when linked together as a system. Rather than focusing on servers, developers can concentrate on writing the essential logic, linking components together, and delegating the heavy lifting to cloud services.

This post presents a sample in-the-wild example, a Beatport Chart Playlist App, to demonstrate how serverless tools in AWS enable the development of a product, from idea to launch, without ever requiring a server.

Real-World Use Case: Beatport Chart Playlist App

I love music. I can’t imagine my day without that. Back in our childhood, we used to trade cassettes to get new music. Discovered new songs from TV, radio charts, etc. However, nowadays, things have become much easier. We all have access to various music platforms (YouTube Music, Apple Music, Spotify, etc.), where almost any music is available. The thing I’m struggling with is getting new music into my collection. I use the Beatport chart to discover new popular electronic music, and then I search for it on YouTube. I’m too lazy, though, to do that manually. That’s tedious, so I automated that.

Let’s take a look at the architecture. This is a music chart processing automated system that web-scrapes Beatport data, sends the scraped data to a server for completion with information pulled from YouTube Music, and then automatically produces public YouTube playlists. The automated system is developed based on an event-driven AWS serverless architecture.

What Is Beatport?

Beatport is one of the world’s largest charts for DJs, producers, and electronic music geeks (looks like I'm the last one). It allows you to purchase high-ranked tracks across various genres, including house, techno, drum & bass, and trance, with daily and weekly charts that reflect trends within the global electronic music community. DJs often use these charts as a source to find out about new releases and what’s hot in clubs and festivals.

What Is Serverless Architecture?

Technically speaking, serverless is not the same as “no server.” There are still servers, but server management falls exclusively on the cloud provider. You don’t have to write complicated code: only functions and service linking.

On the other hand, conventional methods based on EC2 instances or even containers imply continuous infrastructure upkeep. Serverless eliminates that chore and allows fast prototyping and production-ready apps.

Core elements:

Event-driven. Function execution is triggered by events such as API calls, file uploads, or schedules
Auto-scaling. Automatic adjustment of capacity based on workload
Pay-as-you-go. You pay only for the actual execution time
No server management. No patching, scaling groups, or manual deployments

Typical Serverless Architecture on AWS

AWS provides a complete ecosystem for building serverless systems. Imagine it as a miniature city, with every service a specialized guild or district:

API Gateway or App Runner, the city gate. Where travelers (clients) enter, requests are inspected and routed to the right street.
AWS Lambda, the guild of artisans. AWS Lambda forms the heart of our Lilliputian workforce: a guild of agile craftsmen that show up only when invoked. These artisans are assigned specific tasks, complete them efficiently, and then disappear once their job is finished. In real-world terms, it allows developers to run code without provisioning or managing servers. Lambda scales automatically and charges only for the compute time used. This makes it perfect for event-driven workloads, where small and well-defined functions handle data processing, API requests, and automation with minimal overhead.
EventBridge, the clocktower. The scheduler that chimes on time, retries missed beats, and records every strike in the chronicles.
S3, the granary & archives. Where each day’s harvest (raw chart data) is neatly stored, versioned, and aged into cheaper cellars.
DynamoDB, the registry hall. Every track is a citizen with a unique ID. Updates enrich records, no duplicates, no chaos.
Athena, the great library. Think of Athena as the Great Library of our Lilliputian city: a vast reading hall where scholars can open any scroll (JSON file) stored in the Archives (S3) and ask questions in the universal language of SQL. With Athena, there’s no need for complex ETL pipelines or separate databases; the data remains where it was collected, yet becomes instantly searchable. You can analyze historical charts, compare weekly movements, or identify artists that consistently appear in top positions, all without moving data or maintaining additional infrastructure.
CloudWatch, the watchtowers. Sentries observing the city: metrics, logs, and alarms across every guild.

This ecosystem is elastic, cost-efficient, and self-maintaining, where each service is a specialist contributing to a seamless system.

Data Flow

In our Lilliputian city, the data journey resembles the work of many coordinated guilds:

The scraper scouts (Lambda). At dawn each week, the scouts venture to Beatport to collect the newest Top 100 tracks and return with the harvest of data.
The archivists (S3). They carefully store every harvest in well-labeled baskets - JSON files organized by date, preserving both current and historical charts.
The messengers (SQS/SNS). Once the harvest is complete, agile couriers carry the news across the city so that other guilds know it’s time to act.
The processor scouts (Lambda). Upon receiving the news from messengers. They start looking for the new track on the streaming service.
The scholars (DynamoDB). Inside the Registry Hall, scholars catalog each track as a new citizen, verifying whether it already exists and enriching it with new details from YouTube.
The herald (EventBridge). When the last record is written and the charts are ready, the Herald rings the Clocktower bell, signaling the Playlist Guild to publish a new YouTube playlist for the people.

Through this network of tiny but skilled citizens, data flows seamlessly, from discovery to publication, without a single human having to manage a server.

S3, The Granary of Chart Data

Amazon S3 serves as the primary storage layer for raw chart data in our music processing pipeline. It provides a cost-effective, reliable, and scalable solution for storing JSON snapshots of scraped music charts, making them available for both real-time processing and future analytics through services like Amazon Athena.

Why use S3 for Chart Data Storage

S3 is ideal for this use case because:

Cost-effective. Pay only for what you use, and automatically move unused data backward
Reliable. 99.999999999% (11 9's) durability ensures data won't be lost
Analytics-ready. Direct integration with Athena, QuickSight, and other AWS analytics services
Event-driven. Native S3 events trigger downstream processing automatically

Data Structure and Organization

Our chart data follows a hierarchical structure that enables efficient querying and lifecycle management:

charts-vibe-playlists/

├── beatport/

│ ├── 2025/

│ │ ├── 08/

│ │ │ ├── 30/

│ │ │ │ └── top100-120000.json

│ │ │ └── 31/

│ │ │ └── top100-121500.json

│ │ └── 09/

│ │ └── 01/

│ │ └── top100-123000.json

The BeatportScraperFunction is responsible for collecting chart data and storing it in S3. When new files arrive in our bucket, with the help of S3 events, our workflow proceeds, triggering the next artisan to perform their task.

We add a lifecycle policy that moves data to IA/Glacier in 30/60… days and removes objects in 365 days (this is an example).

Benefits for Analytics

This S3-based approach enables powerful analytics capabilities:

Historical analysis. Track the chart evolution over time
Athena queries. SQL-based analysis without data movement
Cost optimization. Automatic lifecycle policies move old data to cheaper storage classes
Data Lake integration. Easy integration with other AWS analytics services

The stored JSON files serve as both the trigger for real-time processing and a historical record for future analysis, making S3 the perfect foundation for our music chart processing pipeline.

EventBridge, The Clocktower

EventBridge is a serverless event bus that makes it easy to connect applications using data from your own apps, integrated Software-as-a-Service (SaaS) applications, and AWS services. The solution utilizes it as a powerful and reliable scheduler to periodically trigger the BeatportScraperFunction.

Why would I even use EventBridge over a simple cron job?

You could certainly use a cron job running on an EC2 instance (you should have an EC2 instance, though) or even a Lambda artisan (easier, but it requires workarounds) – EventBridge offers several advantages for cloud-native applications:

Centralized management. Your scheduled rules are all in one place, defined as infrastructure-as-code right in our template.yaml. This makes them easily traceable, versioned, and managed.

Enhanced reliability & retries. EventBridge has built-in retry policies with exponential backoff. The failure of the target Lambda function, or its throttling, will be automatically retried by EventBridge, enhancing the resilience of our scraping process. So, if the Beatport website is down when our scouts go for the weekly hunt, they would wait till the mammoth comes back.

Deep integration & logging. It integrates well with AWS services. Every invocation attempt will be logged in CloudWatch; rest assured that your scheduled tasks ran, when they ran, and whether they succeeded. The tale of the great quest won’t be forgotten.

Decoupling. The scheduler, EventBridge, has no linkage with the artisan, Lambda. We can modify the schedule without changing the Lambda code or logging into an EC2 instance to reschedule. We could even route the same schedule to multiple squads.

DynamoDB, The Registry Hall

At the heart of our system lies Amazon DynamoDB, the single source of truth for every track. A highly performant and scalable NoSQL database ensures data integrity and enables powerful features.

Why DynamoDB?

We chose DynamoDB for several key reasons that are critical to our application:

Fast, predictable lookups. The main challenge is how to identify whether we’ve previously seen a particular track quickly. Instead of scanning the entire database for every incoming track (that could be pretty costly), we create a deterministic track_id as the partition key by hashing the track’s title and artist and updated_at as a sort key. This way, we can utilize the GetItem operation provided by DynamoDB, which provides access directly to a specified item. This is the cornerstone of our deduplication strategy.
Preventing duplicate entries. Before saving any new track from the scraped chart, we first generate its track_id and check if an item with that ID already exists. If it does, we don’t create a new record; instead, we optionally update the existing one with the latest information, such as its current chart position. If it doesn’t, we create a new record. This simple “check-then-write” logic prevents the database from getting filled with redundant data.

Centralized metadata and enrichment. With the registry hall, we get a single source of truth for all metadata that describes a track. In the case where the music service identifies a matching video for a track, it doesn’t retain that information locally but instead updates that track’s record in DynamoDB. Consequently, our data becomes further enriched, hence more useful for subsequent playlist creation. We can easily extend this to store links from Spotify, Apple Music, or any other platforms. We just need to hire some more artisans who are working with new platforms.

Athena, The Great Library

Our system scrupulously collects weekly snapshots of music charts and stores them as JSON files in an S3 bucket. This creates a valuable historical dataset. But how do we ask complex questions about this data, like "Which tracks are trending upwards?" or "Which artist had the most staying power in the top 10?" Loading terabytes of JSON into a traditional database for analysis would be slow and expensive.

This is where AWS Athena comes in. It allows you to query JSON data directly from files using SQL queries. So you could easily adopt it to execute intricate queries. The possibilities are limited by your imagination only.

Other Guilds

While our main serverless stack covers the core functionality, a few supporting AWS tools complete the picture and make the system production-ready:

CloudWatch, Monitoring & Error Logging

CloudWatch provides centralized logging for all the Lambda invocations, EventBridge events, and even S3 or DynamoDB activity.

With it, we can:

Track execution metrics, including duration, memory usage, and number of invocations.
Set up anomaly detection through alarms for high error rates or throttled invocations.
Debug distributed workflows using CloudWatch Logs Insights with log filtering on request_id across functions.

By default, AWS creates a log group for the Lambda function with an easily recognizable name:

Secrets Manager, API Tokens

Since our app interacts with external APIs, such as YouTube Music or Spotify, credential management is essential.

AWS Secrets Manager securely stores API keys and automatically rotates them as needed. To create a playlist on YouTube, we need to use a YouTube Data API key and store it. For other streaming services, it's nearly the same.

Each Lambda retrieves credentials at runtime using IAM permissions, removing any need to hardcode or commit sensitive data. This setup ensures security, maintainability, and compliance, even as we scale to multiple music providers. Despite its great benefits, we could use a parameter store to minimize costs.

SAM/LocalStack, Local Development

Building serverless apps used to mean constant deployments to AWS, but with AWS SAM CLI and LocalStack, we can develop and test the entire stack locally.

Using these tools:

Lambdas can run in Docker, mimicking the AWS runtime.
S3, SQS, and DynamoDB interactions can be tested offline.
The same template .yaml is used for both local and production environments, ensuring consistency.

This allows rapid iteration while maintaining full IaC (Infrastructure-as-Code) parity with production.

Cost Analysis

One of the biggest strengths of a serverless stack is its cost efficiency.

Let’s break down the potential monthly expenses of this project under moderate usage (e.g., one scheduled scrape per day, a few thousand invocations). AWS gracefully offers our easy-to-use calculation instrument for these purposes:

Even with continuous daily operation, this app costs less than a cup of coffee per month.

Conclusion

The serverless model changes the way you think about application development. No servers to manage, no scaling headaches. Just well-orchestrated, event-driven components, working together in a team.

Here is what the above architecture provides for a project like ChartsVibe:

Speed. We need only a few days to deliver our concept
Scalability. New computing powers just appear when we need them
Cost-effective. Pay for what runs
Maintainability. Minimize DevOps overhead

Serverless works well for startups and MVPs, but it’s also suitable for long-running products that require a steady state of reliability with minimal attention.

As a quick reference, you can find the PoC codebase in my personal repo:
https://github.com/addicted2sounds/charts-vibe

It definitely requires improvements and most likely fixes, but I’m ok with it, since it does what it should do.

With AWS, you truly get “No servers, just vibes.”

Written by
Borys Oleh
Senior Full-Stack Engineer at Svitla Systems

Developers’ Procrastination Unpack: Causes and Solution Hacks (by Nataliia Romanenko)

Svitla Systems Inc. — Tue, 21 May 2024 10:43:27 +0000

Staring at my task list for this development project, I see the usual end-of-day scene — all the little tickets checked off except for that one big feature at the bottom, which I’ve been avoiding. I tell myself, “No worries, I’ll knock it out tomorrow, swear!” But underneath, I’m frustrated that I keep putting off this complex task.

If this is relatable, then you and me both. Procrastination sneaks up on the best of us now and then. For some people, it’s occasional, but for chronic procrastinators, it becomes a habit that reduces their productivity.

So, if you’re in the procrastination boat, don’t worry. There’s a way out, and we’ll get there — maybe not today, but the journey of a thousand tasks begins with a single…oh look, funny meme. Ok, let’s start.

Why Developers Procrastinate and How They Handle It

Reasons for procrastination come in many flavors. Perfectionism, lack of priorities, or poor time management are just the ones that come to my mind first. Luckily, the psychologists have already offered some methods to handle the problem. Moreover, our team members have bravely opened up about their experience dealing with procrastination. So, let’s overview each reason and its possible solutions.

Reason #1. Perfectionism

Aiming high can be helpful, but unrealistic expectations of perfection can paralyze you. The fear that the outcome won’t meet impossibly high standards can stall starting altogether. This cycle of wanting flawlessness yet dreading mistakes hampers getting things done. A 2017 study found those prone to perfectionism were also more likely to put things off.

My procrastination is also often fueled by perfectionism. Take this article, for example — I kept putting off writing it because I was worried it wouldn’t turn out as great as I envisioned. The fear of not doing justice to our team members’ experiences also held me back. To overcome this, I’ve found that setting a strict and near deadline works for me. When I commit to delivering by a specific time, there’s no room for missing the deadline.

Productivity Hacks from the Svitla Team

Kostiantyn Bilyk, Mobile Practice Lead, Team Lead and Lead iOS/macOS Engineer: “In my case, perfectionism is a part of my personality. I always aim to do the task perfectly and in the best possible way. For sure, sometimes it causes procrastination. I start thinking that if I can’t do the task perfectly, I’d better not do it all. On the one hand, it’s a nasty treat. Yet, if we think about it from another perspective, there are some positive sides. When faced with complex tasks, I tend to put them on hold and focus on simpler ones. This way, I stay productive. I also find the 80/20 rule, known as the Pareto principle, helpful in my work. If a task is complex, I tackle its core first and then create a follow-up ticket for the remaining aspects. Surprisingly, more often than not, these follow-up tasks become unnecessary over time. Now, let’s move on to the real-life example. In 2014, I faced a challenge to develop a telemedicine video chat platform, a concept ahead of its time, given the technological constraints of 2014–2015. I treated this task with my perfectionist attitude. So, I took one week to do some research. And another week to implement my idea. By the end of the first week, I found the ideas I liked and presented them to the project manager. After their approval, I started implementing them. By mid-next week, I had finished the platform’s basic functionality — to start and end a call. After that, I presented it to the customer, who was quite satisfied. Soon after, I found out, that the other development team had been working on the task I’d done in 2 weeks, for 3–3.5 months.”

Nadiia Chumak, Senior Go Developer: “I began to notice my tendency to procrastinate since my school days, right from childhood. I understand there must always be a perfect version when doing homework or writing code. Now, I’ve gotten a bit better with that. So, when I see myself spending time on things already working fine, I try to stop and push the code. It helps me get things done faster.”

Reason #2. Absence of Instant Results

We’re used to getting things fast now. But big projects take time before you see results. We get tempted to procrastinate when we don’t get that quick feeling of finishing something. We immediately switch to more manageable things that make us feel good, like checking our phones. To fix this, set small milestones to finish the big project. Breaking it into smaller steps makes each piece feel less complicated, and finishing each step motivates you to continue.

Productivity Hacks from the Svitla Team

Andriy Arysmyatov, Senior .Net Developer: “Facing a task that initially seems manageable, but as you delve into it, the results remain elusive. It gets postponed, especially when it’s not a top priority. The cycle of delay continues until you find a moment of free time. You keep postponing, pushing it aside, and eventually, it slips from your mind. Instead of actively combating this tendency, consider breaking down the task into smaller subtasks. It’s essential not to harbor illusions about the ease of completion. Challenges arise, and acknowledging them is the first step. Planning subtasks helps tremendously. Allocate dedicated time rather than tossing the task aside. Setting specific plans and sticking to them is crucial, ensuring that the task doesn’t get lost in daily activities.”

Reason #3. Unclear Understanding of the Final Result

Fuzzy goals stall momentum. Laser focus fuels drive. Define projects concretely — what must happen and when. Broad assignments invite procrastination. Specific, scheduled tasks kindle motivation. Chop large efforts into bite-sized pieces. Rather than “finish report” in the foggy future, spot “Draft intro section Tuesday, 10 AM-12 PM” in the calendar now. Granular goals crystallize the path from concept to completion. They transform vague aspirations into timed action steps. Clarity breeds productivity. Hone objectives. Then, accomplish each scheduled session one at a time.

Productivity Hacks from the Svitla Team

Pavlo Zubenko, JS Lead: “To tackle procrastination, I’ve got a few tricks. If it’s a big task, I take a break — maybe grab a slice of pie or brew some coffee. It’s a mini reward to keep things rolling. During a hefty project, I take short breaks, get some coffee, or snack a bit to stay sharp. I ditch my phone and stick to using a notebook to dodge distractions. Magnetic doodle pads and a whiteboard notepad help me sketch out ideas and smoothly switch between different parts of the task. Taking a step back and looking at the big picture helps gain some perspective. As I near the end of a task, I follow a simple rule: no starting anything new for precisely 20 minutes. It’s a way to wrap things up”.

Yurii Stasuik, Senior Front-End Developer: “When you’re working on a product but don’t get the expected result, it messes with your motivation. Being a front-end developer is not just about coding. You also have to think about the user interface and get feedback, which is not always there. The fix? Well, maybe switching up the project is the easiest option. For me, also doing A/B tests work. This way, I get the necessary feedback, keep myself motivated, and improve the final product.”

Reason #4. Lack of Self-Discipline

Pushing through routine or tiring tasks takes self-control. When self-discipline is lacking, procrastination often happens instead. Building good daily habits boosts focus and avoids distractions. This means limiting social media, turning off notifications, and potentially using website blockers. Start each day by working on priorities, not checking emails. Taking regular breaks to rest fully recharges energy and concentration. The key is structuring consistent conditions focused on concentration until self-discipline becomes a habit.

Productivity Hacks from the Svitla Team

Yurii Stasuik, Senior Front-End Developer: “Balancing work, home stuff, family, and other tasks can be challenging, especially when working from home. I’ve established clear boundaries to combat procrastination due to a lack of self-discipline. I avoid working in bed, dedicating 3–4 hours to focused work, followed by a break. This approach helps me maintain productivity and balance within my responsibilities.”

Reason #5. Task is Too Difficult

We often put off work that seems too hard, or we don’t feel skilled enough to complete it. Tasks with unclear instructions or too many complex parts can make us freeze up and avoid starting. To fix this, break big intimidating goals into small bite-sized tasks. This outlines a path to slow but steady progress.

Look at what skills you need to develop and leave enough time for improvement. As you build momentum step-by-step, what felt impossible starts to seem within reach. Check in often to adjust expectations before frustration creeps in. Bit by bit, seeing progress dispels the notion that the level of difficulty can’t be handled. Structuring overwhelming tasks into achievable pieces avoids putting them off.

Productivity Hacks from the Svitla Team

Kristina Shatts, Senior Node.js Developer: “Currently, I rarely procrastinate due to overly complex tasks. There was a time, especially when I was a beginner, when I was afraid to work on challenging tasks, making me nervous. To tackle this, I tried to break down the tasks into smaller sub-tasks to understand them better and deal with each part separately. Seeking assistance was also important. Not just from colleagues, but also by reaching out to acquaintances, friends, and individuals knowledgeable in the field. Asking questions was something I learned not to fear. There is nothing wrong in not knowing something”.

Kostiantyn Bilyk, Mobile Practice Lead, Team Lead and Lead iOS/macOS Engineer: “In 2019, I started to lead work on the new project. Its idea seemed almost unrealistic — creating a next-gen file manager with meta-connections between files. The project involved mobile/desktop platform support, Apple sign-in, and working without servers. It seemed daunting from an engineering perspective, but the project looked interesting, long-term, and promised team expansion.

Initially, I was overwhelmed. To overcome this state, I adopted the eat-the-frog technique. Each day, I set one challenging but doable task — the “frog” — to tackle. This simple strategy helped maintain a steady workflow.

Another useful thing for me was incremental growth. Starting with basic functions, I gradually expanded the functionality. As the project grew, we added AI-driven features and integrated chat support. I needed a holistic vision, breaking the project down like a pyramid and building it up iteratively.”

Reason #6. Boring Task

Tedious tasks need a fresh take. What could make this better? Look for hidden upsides and chances to grow. Get creative — can you mix fun with work? Make dull duties more lively. Add enjoyment into tasks. Reframing can shift your mindset, fueling motivation. Find enriching bits in each chore. Tweaking your view lifts your mood, lightening the workload. Soon, getting things done flows faster, building your willpower.

Productivity Hacks from the Svitla Team

Kristina Shatts, Senior Node.js Developer: “If there’s something new and exciting, I’m all in, but the not-so-interesting stuff, like messing with database scripts, tends to make me procrastinate.

To tackle this, I’ve got this Pomodoro technique going on — work hard for 30 minutes, then take a 10-minute break to do something fun as a reward. I’m a pretty sociable person. So collaborating with colleagues on tedious tasks also works for me.

Finally, if I’m doing routine stuff the whole day, I take more time for breaks afterward — solving puzzles, enjoying some tea, or just chatting with someone to shake things up.”

Reason #7. “I’ll Do It Later”

The mindset of “I’ll do it later” leads to endless delays. Unpleasant tasks often get pushed to some vague future time to avoid doing them now. But later rarely comes, creating a cycle of stress and rushed last-minute work. To break this, firmly decide what will get done today, not someday. Tying daily goals to values, not just distant obligations, makes completing them more meaningful.

Productivity Hacks from the Svitla Team

Kristina Shatts, Senior Node.js Developer: “If there’s no deadline, I tend to postpone things. But funny enough, I do things without a hitch when there’s a deadline.

With bigger tasks, I convince myself I can knock them out quickly and decide to do them later. Yet looking back, I understand that it’s not the best approach. At first sight, the task may seem less time-consuming than it is. In this case, the best way is to look closer at the task and estimate its priority from the beginning.

Reason #8. Lack of Energy

When energy is low, big tasks can feel impossible. But waiting until we feel perfectly energized to start important work can cause endless delays. Regular routines for sleep, diet, exercise, and renewal activities help manage daily energy. To combat a lack of motivation, know when you tend to focus best and tackle critical projects.

For draining items at low-energy times, briefly work in 20–30 minute intense bursts, then wholly rest. If you often feel exhausted, carefully spend high-value energy on priorities while conserving strength to recharge. With enough built-in self-care, you store vitality instead of endlessly waiting to feel energized first before starting.

Productivity Hacks from the Svitla Team

Andriy Arysmyatov, Senior .Net Developer: “Whenever my motivation takes a hit, I find it crucial to take a break. I step away and read something, but I always try to maintain focus. Getting a good night’s sleep or walking helps recharge my energy. When I return, I make it a point to create a clear plan.

Purposeful procrastination involves taking a break to rest and returning with a fresh perspective. Switching to another task and returning later often leads to a quick resolution within 1–2–3 hours.

I also contact my team for help when needed. Sharing my feelings and challenges with colleagues can make a significant difference. Having a trustworthy manager’s support is also crucial in such situations.”

Yurii Stasuik, Senior Front-End Developer: “Procrastination hits me quite often, especially after an initial burst of energy. I’ve figured out that taking a moment to recharge and switch things up helps. I turn to things like snowboarding, hiking the mountains, or going on a trip for an energy boost.

Procrastination is less common at work, but it tends to sneak in when I’m dealing with something new or uncertain. I’ve got some tricks up my sleeve, but handling procrastination is an ongoing struggle. It’s all about finding that balance and grabbing energy from different things I enjoy doing.”

Reason #9. Mental Issues

Putting things off can sometimes come from underlying mental health issues. Symptoms like depression, anxiety, ADHD, or chronic stress drain motivation to start and stick to boring work. Without treating the root causes, just trying to push through backfires.

For example, emotional paralysis may benefit from scheduling required activities each day. Perfectionism from anxiety might require reframing unhelpful thought patterns. Those with ADHD can use external accountability tools to manage distractibility. When mental troubles obstruct average productivity, the most critical step is acknowledging the needs.

Productivity Hacks from the Svitla Team

Pavlo Zubenko, JS Lead: “As a kid, I was diagnosed with dyslexia, especially dysgraphia. It didn’t stop me from becoming a developer. Yet I have some challenges to overcome. Reading tech docs can be a bit of a struggle. The sentences don’t flow smoothly, and there’s always something interesting popping up to distract me. But, you know, you get used to it, and I’ve figured out some tricks to handle it. For instance, I found out that a power nap is really helpful — it’s a short, 15–30 minutes of sleep, after which I feel recharged and more concentrated”.

Conclusion

Understanding why we procrastinate is the first step to getting things done in the quest for productivity. Whether it’s the desire for perfection or unclear priorities, each obstacle has a solution. With awareness and solutions, we can turn procrastination into a helper, making overcoming challenges easier.

In the end, getting through a thousand tasks might take time. Still, with simple strategies and self-understanding, we’re on a journey where procrastination is a temporary sidekick, not a constant companion.

Leveraging AI in Education: Exploring Big Data and Related Applications

Svitla Systems Inc. — Fri, 22 Sep 2023 11:48:48 +0000

In recent years, technology has become increasingly vital in shaping the landscape of education. From traditional classrooms to online learning platforms, the integration of technology has revolutionized how knowledge is imparted and acquired. One of the significant advancements in this realm is the application of Artificial Intelligence (AI) and Big Data.

Artificial Intelligence has opened up new horizons in education. With machine learning algorithms and data analysis, AI-powered tools personalize education and enable tailored instruction, immediate feedback, and adaptive assessments, optimizing the learning experience for each student.

Additionally, AI-driven virtual tutors and chatbots are gaining prominence, offering round-the-clock support to learners. These intelligent systems can provide instant answers to students’ queries, offer explanations, and guide students through complex concepts, empowering them to explore and deepen their understanding.

Big Data, on the other hand, plays a crucial role in education by collecting and analyzing data like student performance metrics, feedback from assessments, and even social interactions within learning platforms, generated within the educational ecosystem.

Adapting Artificial Intelligence and Big Data to education holds immense potential as technology advances. It empowers educators to deliver personalized, adaptive, and engaging learning experiences, while providing valuable insights that shape pedagogical practices.

Thus, the growing importance of technology in education, specifically through the utilization of Artificial Intelligence and Big Data, has the potential to transform the learning landscape, optimize educational outcomes, and nurture a generation of digitally fluent and adaptable learners.

Understanding Big Data in Education

In education, the concept of Big Data refers to large volumes of structured and unstructured data generated within the educational ecosystem, including student information, academic records, assessments, social interactions, and more. Big Data in education encompasses the collection, analysis, and interpretation of vast amounts of data to derive meaningful insights and patterns that inform educational practices and policies. It involves leveraging technology and advanced analytics techniques to make sense of the diverse data points generated across various educational systems and platforms.

Big Data in education is characterized by its immense volume, variety, velocity, and veracity. It encompasses diverse types of structured and unstructured data from various sources, including learning management systems and online platforms. The velocity of data generation requires real-time processing for timely interventions and personalized support. Ensuring data veracity is crucial to maintain accuracy and integrity when working with educational data.

Benefits of Big Data in Education

Personalized learning: Analyzing Big Data enables tailored instruction and personalized learning experiences based on individual student needs, preferences, and progress. A study conducted by the Bill & Melinda Gates Foundation demonstrated that personalized learning has the potential to boost student achievement by 7% compared to traditional methods, highlighting its effectiveness.
Early intervention and support: Big Data analytics identify students at risk and enable timely interventions to provide necessary support and improve student outcomes. Numerous states, districts, and schools have successfully acquired the necessary data to monitor student progress and achievement, and some have effectively utilized it. A notable example is Fresno, where the implementation of a new data system revealed a lack of awareness among students regarding their college options. In response, the district took proactive measures by creating personalized college information packets for each senior who met the state’s college requirements. The outcome was remarkable, with a 50 percent surge in the number of students applying to California public universities.
Evidence-based decision-making: Educational institutions can make data-driven decisions by analyzing educational data, evaluating interventions, and enhancing teaching methodologies and resource allocation.
Continuous improvement: Big Data analysis provides insights to refine teaching strategies, improve curriculum effectiveness, and enhance the overall quality of education.

In conclusion, Big Data in education represents a transformative opportunity to harness the power of data analytics and gain valuable insights that shape educational practices and policies. By collecting, analyzing, and leveraging Big Data, educational institutions can unlock new possibilities for personalized learning, early intervention, evidence-based decision-making, and continuous improvement to ensure better educational outcomes for all learners.

The Svitla team has extensive experience integrating Big Data into a wide variety of business processes, including education, and using advanced analytical tools. As a result, we are skilled at developing powerful and tailored software solutions tailored for educational institutions.

The Role of AI in Analyzing Big Data

Artificial Intelligence (AI) has become a game-changer in many industries, including education. Combined with data within the educational sphere, AI algorithms can unlock powerful insights and revolutionize the way educators understand and utilize information. Here, we explore the role of AI in analyzing Big Data in the educational sector.

In education, AI spans personalized learning platforms, intelligent tutoring systems, chatbots, and data analytics tools. AI algorithms process and analyze Big Data to uncover patterns, predict outcomes, and provide valuable insights for educators.

With the increasing digitization of educational processes, enormous amounts of data are generated, including student performance metrics, learning activities, assessments, and more. AI algorithms can effectively process and analyze this data in the following ways:

Personalization: AI algorithms can analyze individual student data to identify learning preferences, strengths, and areas for improvement. By considering various factors such as learning style, pace, and content preferences, AI can personalize instruction, recommend tailored resources, and provide adaptive learning experiences that optimize student engagement and success.
Predictive analytics: AI algorithms can analyze historical educational data to make predictions about student outcomes. By identifying patterns and correlations in the data, AI can forecast potential risks, such as students at risk of dropping out or struggling with specific subjects. This enables educators to intervene early and provide targeted support, improving student retention and academic performance.
Intelligent tutoring systems: AI-powered tutoring systems leverage natural language processing and machine learning to provide interactive and personalized learning experiences. These systems can assess student knowledge, identify misconceptions, and offer targeted feedback and guidance. AI algorithms continuously adapt to student responses, refining the tutoring experience and optimizing learning outcomes.
Chatbots and virtual assistants: AI-driven chatbots and virtual assistants can enhance student support services. These intelligent systems can answer frequently asked questions, provide guidance on course selection, offer study tips, and assist with administrative tasks. By leveraging natural language processing capabilities, AI chatbots can engage in human-like conversations, improving accessibility and responsiveness for students.

Benefits of AI in Analyzing Big Data

Enhanced decision-making: AI algorithms analyze complex educational data sets and provide valuable insights to educators and administrators. These insights inform evidence-based decision-making, enabling institutions to improve teaching methodologies, curriculum design, resource allocation, and student support services.

Personalized learning: AI-powered platforms can adapt instruction based on individual student needs, preferences, and learning styles. By analyzing Big Data, AI algorithms deliver customized content, adapt the learning pace, and provide real-time feedback, promoting personalized and self-directed learning experiences.

Our client offers quality education through a unique teaching methodology based on the emotional, intellectual, and social development of each student. They provide the necessary tools and experiences needed to face the challenges of today’s changing, multicultural, and competitive society. Our client has an international presence, with offices in the United States, Mexico, Spain, and Chile, serving more than 1000 schools.

Efficient data analysis: AI algorithms can process vast amounts of data at a speed and scale beyond human capabilities. By automating data analysis, AI saves time for educators, allowing them to focus on interpreting the insights and implementing appropriate interventions effectively.

Continuous improvement: Through iterative data analysis, AI algorithms facilitate continuous improvement in educational practices. By identifying trends, evaluating interventions, and measuring student progress, AI supports educators in refining their instructional strategies, enhancing curriculum design, and maximizing learning outcomes.

In a nutshell, the role of AI in analyzing Big Data within the educational sector is transformative. AI algorithms offer:

Advanced data processing capabilities
Personalization of educational services
Predictive analytics
Intelligent tutoring systems
Data-driven decision making
Personalized support
Optimized learning experience for students

Enhancing Personalized Learning through AI

Artificial Intelligence (AI) plays a crucial role in advancing personalized learning, tailoring educational experiences, and optimizing learning outcomes. Here, we delve into how AI enhances personalized learning and explore the benefits and challenges of implementing this approach with AI and Big Data.

Personalization based on individual student needs: AI algorithms analyze vast amounts of educational data, including student performance, learning styles, preferences, and progress, to create tailored learning experiences. By considering these factors, AI can customize the content, delivery methods, and assessments to match each student’s requirements.

Our client’s product enables scalable learning by powering engaging online spaces for professional communities to share knowledge. Involved communities enable multi-stakeholder groups to quickly assemble fully tagged, searchable knowledge bases to leverage machine-aided and peer-to-peer content curation. Through a design thinking methodology, peer-to-peer learning environments, and deep analytics, the Product is revolutionizing how knowledge is shared and scaled. The platform helps organizations break down knowledge silos and create a culture of scalable learning. The Service’s Communities empower learners to become participants and producers of knowledge rather than merely consumers of content.

Benefits of AI in Personalized Learning:

Customized content: AI algorithms can curate and deliver content that aligns with the specific needs and interests of individual students. By analyzing data on student performance, AI can recommend appropriate resources, adapt learning materials, and provide targeted enrichment or remediation activities.

For instance, Svitla helped our client to create captivating online courses. The first course is a comprehensive YouTube Influencer course — the only one in the world where the world’s top influencers teach how to create viral original content, develop a massive following online, and monetize your audience.

Adaptive instruction: AI-powered systems can adapt instruction in real-time, providing immediate feedback and guidance. By monitoring student responses, AI algorithms can dynamically adjust the difficulty level, pacing, and sequencing of learning activities, ensuring optimal challenge and engagement.

Our client’s collaborative learning platform empowers organizations to design and deliver experiential learning that accelerates business performance. Global corporations, executive education providers, and training firms rely on this platform to develop high-value capabilities through coaching, mentorship, and group collaboration. Their proven approach to learning connects diverse groups of learners, mentors, and leaders in a high-impact online environment, unlocking organizations’ collective knowledge.

Intelligent feedback: AI can provide timely and constructive feedback to students, highlighting areas of improvement and suggesting personalized strategies for mastery. This continuous feedback loop fosters self-reflection and empowers students to take ownership of their learning journey.

Data-driven insights: AI algorithms analyze educational data to generate actionable insights for educators. These insights inform instructional decision-making, allowing teachers to identify learning gaps, adapt teaching strategies, and provide targeted interventions that support each student’s progress.

Unlock the transformative potential of artificial intelligence (AI) in the educational sphere with Svitla’s in-depth expertise. Svitla’s AI solutions offer boundless opportunities to revolutionize teaching, learning, and administrative processes, fostering a more engaging, personalized, and efficient educational experience.

Challenges of Implementing Personalized Learning with AI and Big Data:

Data privacy and security: It is crucial to ensure the ethical use of student data and to implement robust data protection measures.
Access and equity: It’s essential to bridge the gap between technologies and learners. For example, students from underserved communities must have equal access to AI-powered personal learning experiences.
Teacher training and support: Educators need proper training and support to effectively interpret AI-generated insights, personalize instruction, and strike a balance between technology and human interaction in the classroom.
Ethical considerations: It is vital to address biases, ensure algorithmic transparency, and maintain algorithmic accountability to build trust and maintain ethical standards.

Bottom line
The benefits of AI in personalized learning include customized content, adaptive instruction, intelligent feedback, and data-driven insights. However, challenges related to data privacy, access and equity, teacher training, and ethical considerations must be addressed to ensure the responsible and equitable implementation of personalized learning with AI. With careful planning and support, AI-powered personalized learning has the potential to revolutionize education, empowering each student to reach their full potential.

AI-Powered Educational Assistants and Virtual Teachers

Virtual assistants and chatbots have become valuable assets in the educational sphere. They employ natural language processing and machine learning algorithms to comprehend user queries, provide assistance, and facilitate seamless learning experiences.

AI-powered educational assistants and virtual teachers offer numerous benefits. First, they provide personalized support, tailoring their responses and resources to meet the specific needs of individual students. This personalized approach enhances the learning experience, catering to each student’s unique requirements.

Second, virtual assistants are available round-the-clock, ensuring 24/7 access to help and resources. Students can seek guidance at their convenience, fostering independence and self-directed learning.

Third, AI-powered educational assistants offer scalability and consistency since they handle multiple queries simultaneously, ensuring seamless support for a large number of students. Additionally, they provide consistent responses and feedback, eliminating discrepancies in instructional delivery and promoting equitable access to information.

Finally, the immediate feedback and support offered by virtual teachers and assistants are invaluable. Students receive instant feedback on their work, allowing them to identify errors and areas for improvement in real-time. This feedback loop boosts learning efficiency and facilitates student progress.

Limitations of AI-powered Solutions
It is important to recognize the limitations of AI-powered educational assistants and virtual teachers. They may struggle to replicate the human element found in traditional teacher-student interactions. The personal touch, empathy, and emotional connection that come with face-to-face interactions may be lacking in AI systems. Additionally, AI algorithms may face challenges in addressing complex problems that require critical thinking, creativity, and subjective judgment.

Data dependence and privacy concerns also arise when implementing AI-powered systems. These assistants rely on extensive student data to personalize their responses. Ensuring data privacy and security, as well as addressing ethical considerations surrounding the use of student data, are essential when integrating AI into education.

Lastly, technical limitations may hinder the effectiveness of AI-powered assistants. The accuracy of these systems in understanding and responding to complex or contextually nuanced queries can be affected. This may result in incomplete or incorrect information being provided to students.

Bottom line
AI-powered educational assistants and virtual teachers have the potential to greatly enhance education delivery. Their personalized support, 24/7 availability, scalability, and immediate feedback bring significant advantages to the learning experience. However, limitations such as the lack of human interaction, challenges in complex problem-solving, data privacy concerns, and technical constraints should be taken into consideration. Striking the right balance between leveraging the benefits of AI and maintaining the essential role of human educators is crucial for a comprehensive and successful educational experience.

Final thoughts

AI-powered educational assistants, virtual teachers, and personalized learning systems offer numerous advantages, including personalized support, scalability, 24/7 availability, immediate feedback, and data-driven insights. These technologies have the power to enhance the learning experience, bridge educational gaps, and empower both students and educators.

However, it is crucial to approach the integration of AI in education thoughtfully and responsibly. Ethical considerations, data privacy, inclusivity, and the role of human educators must be carefully addressed. Collaboration between educators, researchers, policymakers, and technology developers is key to navigating the challenges and maximizing the benefits of AI in education.

The future of education lies in striking the right balance between leveraging the power of AI technology and maintaining the essential elements of human interaction, empathy, and creativity. AI should be viewed as a powerful tool that enhances and supports the work of educators, rather than replacing them. The role of educators will evolve to include utilizing AI-generated insights, personalizing instruction, and fostering critical thinking and socio-emotional skills.

As we move forward, it is important to prioritize investments in teacher training, research, and equitable access to AI-powered educational tools. That’s how we can create a future where personalized, inclusive, and lifelong learning becomes the norm, empowering individuals to thrive in a rapidly changing world.

Svitla Systems helps clients select and implement AI and Big Data solutions that perfectly align with their unique business requirements. With in-depth knowledge and expertise of AI and Big Data technologies, our engineers seamlessly integrate these solutions for our customers.

Svitla representatives can help you explore how we can collaborate strategically to enhance your adoption of AI and Big Data solutions. Contact us and we’ll get back to you shortly.

Why Every Digital Product Needs a UX Writer — Mariia Vynogradova, Senior UX Writer at Svitla Systems

Svitla Systems Inc. — Thu, 29 Jun 2023 12:59:27 +0000

User experience (UX) is a crucial element in the design of digital products, and it should never be underestimated. A successful digital product that effectively engages its audience stands out from those that fail to provide a positive user experience. Overall, the importance of UX for digital products boils down to one thing: creating a great user experience as the foundation for creating a successful and sustainable product.

For years, Svitla Systems has been dedicating a significant amount of resources, talent, and time to perfect its UX services and delivery, engaging, harnessing, and learning from top experts in the field to ensure every project is approached with a strong UX initiative from the get-go.

For today’s piece, we held a candid interview with one of our greatest: Mariia Vynogradova, our UX Writer who’s changing the world of our clients’ digital product development.

Let’s get going!

Let’s hit the basics of who UX Writers are

The phrase ‘Who is a UX writer’ is typically a starting point for anyone seeking to understand this unique role within the field of user experience. I would best explain UX Writing as the process of creating clear, concise, and user-focused content for digital products and interfaces. The primary goal of UX writing is to enhance the user experience by providing helpful, relevant, and engaging copy that guides users through the product.

The user experience is pivotal when designing digital products because it ensures digital products are effective and efficient so users get a positive and satisfying experience. Google is suggesting you an article based on your interests? Netflix is auto-playing the next episode so you don’t need to touch the button? Spotify is wrapping up your playlists at the end of the year so you can share them on Instagram? It’s all about the user experience. By focusing on UX, you can identify and eliminate pain points in your product, streamline processes, increase customer satisfaction and loyalty, improve customer retention, and even attract new customers through word-of-mouth referrals. As they say, UX design can make or break your product and relationship with customers.

“What is UX writing and how it differs from copywriting?” is a question I wish all recruiters would research before starting to look for a UX Writer 😂. Essentially UX writing focuses on the user’s needs and goals rather than marketing or promotional messaging, simple as that. It involves writing microcopies, such as buttons, labels, tooltips, error messages, and other interface elements that help users navigate and interact with the product. UX writers work closely with designers, developers, and product managers to ensure that the copy and its tone align with the product’s strategy.

As a part of the UX team a UX Writer puts together words and creates copy for interfaces. By saying “words” I mean literally all the words that comprise a digital product. From buttons, headings, navigation hints, and more — every single word that a user sees is written by the UX Writer.

Sounds easy? Try explaining what the concept of “Cookies Allowlist” is in one sentence so it’s clear for your colleagues, your children, and your granny ;)

It took over a decade to realize that designers aren’t ready to work with words, and copywriters have a very vague concept of UX and development. But we get better at this :)

Some of the key responsibilities of a UX writer include:

Creating and editing copy: Write clear and concise copy that is easy to understand and use; the UX Writer should also edit copy to ensure that it meets the needs of the product or service.
Conducting user research: Conduct user research (with UX researchers if you are lucky enough to have them!) to gain insight into user needs and behaviors, and to ensure that content is aligned with user expectations.
Collaborating with designers, developers, product managers, support, and researchers: Ensure content is cohesive and integrated effectively into the product or service.
Creating style guides to maintain the voice and tone of a product: Create style guides and standards for a consistent voice and tone across all user-facing content.
Testing and iterating: Test, iterate, and audit content to ensure that it is clear, accessible to all users, and meets the needs of the user.

When thinking about extraordinary skills or responsibilities, I foresee:

Having empathy and a user-centric way of thinking.
Possessing knowledge of wireframing/prototyping tools.
Conducting design thinking workshops.
Being data-driven.
Proficiency in design collaboration tools (Sketch or Figma) and text collaboration tools (Notion, Confluence, etc.).
Knowing the tools to conduct UX research and analyze data is a great plus.
Sometimes localization tools are required.

When Does Your Project Need a UX Writer?

Having a UX writer is beneficial for every digital product and it brings tons of value when:

The team needs to make anything from scratch.
When an existing product or service is being rebuilt.
When a product or service is released in many markets.
Whenever there is a need to write, revise, or enhance a product’s copy.

Here are the main areas that showcase the significant contribution UX writers bring to their teams and products.

Crafting clear and consistent messaging: by using the language that resonates with the target audience, UX writers ensure users can easily understand what the digital product is about and what they can expect from it.
Developing content that facilitates user flow: UX writers help map out the user journey, creating content that guides users through the digital product with ease by ensuring information is logical and intuitive.
Enhancing accessibility: They keep accessibility in mind by using plain language, providing alternative text for images, and ensuring content is easy to navigate for users with disabilities.
Testing and refining content: UX writers work closely with designers and developers to test digital product content and refine it over time by analyzing user behavior and feedback.
Building brand awareness: By helping create content that reflects the brand’s voice and values, they ensure that messaging aligns with the brand’s identity.

Negative Consequences of Delegating UX Work To Non-UX Writers

I’ve found that delegating UX work to non-UX writers legitimately causes headaches and bad results overall. Why? Firstly, non-UX writers lack the specialized skills and knowledge required for creating effective user experiences. As UX Writers, it’s ingrained in us to have a deeper understanding of user psychology, design principles, and technical considerations, and without this expertise, non-UX writers may create content that is confusing, ineffective, or even counterproductive, leading to a poor user experience.

Secondly, I’ve seen firsthand how delegating UX work to non-UX writers leads to inconsistencies and errors in messaging, tone, and style; not to mention, they typically don’t fully understand the importance of accessibility and inclusivity in user experience design.

Why does UX work keep being delegated to non-UX writers? Well, unfortunately, I’ve come across the fact that, for organizations, it’s tricky to recognize the importance of UX. Also, organizations typically prioritize cost-cutting measures, leading them to delegate UX work to existing staff members who lack the necessary expertise.

Real-life Examples of Great UX Writing

The first product that comes to my mind is Duolingo because I’m trying to improve my German and Japanese now. The app is packed with great gamification and UX Writing, exemplifying a consistent, well-executed design strategy that is intuitive and user-friendly for anyone to use.

Next up is Canva. Upon opening the app, it asks users to learn about how they plan to use the platform and then leads them to a tutorial focused on what they actually want to do rather than taking them through everything. This is particularly great because it underlines the unique experience “especially for you.”

Also, I really appreciate the fact that the subscription stage is not annoying 😀

Next, I couldn’t leave Netflix out as it’s a perfect example of an intuitive UX. Users don’t need further guidance because the product is extremely simple. From asking for a few preferences to suggest related recommendations to its simple and funny UX wording, it’s a great UX experience all around.

Slack is also a great example of an engaging experience. From the get-go, it encourages new users to learn the product by actually using it. After signing up and asking 3 questions about your team, the app immediately starts showing users around, starting with the very first project channel.

Another cool UX aspect of Slack is its use of contextual onboarding to bring information wherever it is needed. For example, when a user wants to integrate an app with their Slack workplace, a tiny three-step user onboarding flow appears.

Last but not least, Notion. It has a three-step sign-in session, helping users personalize the workspace to match their specific needs and showing straight away that Notion is a powerful and multifunctional solution.

Instead of dropping new users into a complex dashboard, Notion introduces them to the core features with a lightweight and interactive tutorial. I especially love the brief, hands-on onboarding that shows the product’s key features, functionalities, and commands.

Another burning question that many ask is whether or not I feel threatened by AI tools such as ChatGPT. My answer is “Not now”. First of all, most of my work takes place before I even start typing. As of today, there is no AI to interview users, conduct UX research, go to meetings with product teams, or have discussions with designers and developers. Plus, AI is way too polite and in my area sometimes you need to fight to prove your opinion ;) By the way, AI gave me some pretty good ideas when I was preparing for this interview 😌

UX Writer’s top recommended reading list should include a few books:

Strategic Writing for UX by Torrey Podmajersky
Microcopy: The Complete Guide by Kinneret Yifrah
Content design by Sarah Winters
Writing Is Designing by Michael J. Metts & Andy Welfle
100 Things Every Designer Needs to Know About People by Susan Weinschenk
Essentialism: The Disciplined Pursuit of Less by Greg McKeown
Hooked: How to Build Habit-Forming Products by Nir Eyal
The Design of Everyday Things by Don Norman

As well as podcasts, here I would recommend listening to Writers in Tech; courses UX-writing Hub and for general information Google UX design.

Overall you need to read, listen, highlight, and write ideas down. See something interesting? Make a note of it. Surround yourself with words and look around to spot good and bad UX copy everywhere, you’d be surprised! It’s my biggest inspiration.

How to find a professional UX Writer for your project?

Should you be looking for a boost in your digital product’s user experience, we at Svitla Systems, have a team of UX writers who are adept at simplifying complex ideas, translating technical jargon into plain language, and creating a unified voice across all user touchpoints.

Our UX writers understand the importance of user-centric content. They are always focused on refining their work, ensuring it effectively meets user needs and enhances the overall user experience. In the realm of digital product development, the significance of UX writing can’t be overstated. It’s about crafting clear, concise, and user-focused content that helps users achieve their goals effortlessly. Whether you’re starting a new project or looking to improve an existing one, we’re here and happy to assist with your UX writing needs.

Let us demonstrate how effective UX writing can elevate your user experience to the next level!

Best Test Management Tools In 2023. Comprehensive Review. (By Sergii Vashchenko, QA Practice Lead at Svitla Systems).

Svitla Systems Inc. — Thu, 20 Apr 2023 12:57:34 +0000

Hi there! My name is Sergii Vashchenko, and I am the QA Practice Lead at Svitla Systems. I enjoy trying new tools, techniques, and theories on the projects I am a part of and would like to share my experience with the rest of our QA community.

In the last ten years, I've used many tools for test management. I already have a few favorite ones, but with 2023 making waves, I became interested in finding the right tool to suit current and modern project needs for less money.

Nominees

Nowadays, there are tons of tools for test management. I had to narrow the scope, so I focused on the ten most popular tools based on the tech reviews from technical resources and QA forums. Then, I reviewed the tools documentation and picked up the five most promising ones for my current project: TestRails, X-Ray, Zephyr Scale, AIO Tests, and QA Deputy. Many of them are some of the most prominent players on the market, and I'd be willing to bet good money that you have already worked with at least two of them. However, they have different philosophies and sets of features, and the pricing differs significantly. So it's fascinating to compare them side by side doing the same things for the same project.

Assessment Criteria

To make a comparison, I analyzed project requirements and prepared a list of crucial criteria:

UX.
The most crucial criterion for the tool is an outstanding user experience. Overall it should speed up the process of writing tests and managing test executions. Nobody likes to do unnecessary clicks or to be unable to find the required stuff quickly.
Test reports and time tracking.
Statistics and estimations are beneficial for making decisions. Therefore having good reports is essential.
Requirements traceability.
The traceability between tests and user stories will help ensure that everything necessary has been considered.
Gherkin language support.
Test cases could be written in BDD style, so the tools should support the Gherkin language.
Automation.
The integration with GitHub Actions and good documentation for the test automation API is a must.
Performance.
The app should work quickly enough or it will become an annoying thing that will quite quickly distract engineers from their work.
Price.
The tools should be relatively inexpensive for a small team. However, the team will start to grow fast in the future.

Other additional criteria we include.

Jira application.
The team prefers storing tests and other development tickets in one place, so direct test management from Jira was considered a big plus.
Test data import/export. It would be good to have the option to export the test cases and test suites into some standard formats like ".csv" or ".xml ."It will allow us to migrate the test base to a new test management tool if there is a need with minimum effort.
Free trial. Trying the app alone can provide a much more user experience than watching video demos or reading product documentation.

Test Tool’s Test Drive

The tool's test drive included the following things:

Initial tool setup
Making a test set for a new user story
Making test cycles
Making requirement traceability
Time tracking
Test reports
Executing prepared tests

On average, I've used each tool for new projects for at least one week. Below is a short overview I've prepared for each tool, where I point out my impressions of each tool and omit some commonalities. Also, please remember that pros and cons are subjective, so take them with a pinch of salt!

TestRail

Home Page: TestRail: Test Management & QA Software for Agile Teams

Version under the test: 7.5.6

Overview. TestRail is a standalone app and you must consider that it takes a few minutes to register, get an instance, set up the integration with Jira, and start writing tests. You are unable to create and keep tests directly in Jira. However, installing a free Jira plugin can enhance your features. Overall, the tool allows you to link tests to Jira tickets and monitor test status from Jira. Additionally, it is possible to open bugs directly from the TestRail tool.

The TestRail has a unique feature that others don't. I am talking about UI scripts. This feature allows you to add some additional things on your own. For example, you can make custom colors for keywords inside the test cases through reminders when some specific page/test is opened, etc. You can customize almost everything inside the app (add custom templates for test cases, a company logo for test reports, and much more). The TestRail API can be used with lots of test automation frameworks.

Pros:

Reliable, stable, and fast
Intuitive interface for tests, test execution, and test management overall
Clean and easy way to monitor the testing status
Time tracking for each test as well as for test run/test plan
50GB of free cloud storage for test data (screenshots, scripts, videos, etc.). Note: you can have more storage space, but each additional 25 GB will be billed additionally to your regular subscription.
Custom fields for tests and test templates
User variables for the test automation
Good looking reports
Lots of options for different tools integration and test automation

Cons:

Quite pricey (37$ per user monthly or 34$/user monthly if you pay for a year).
The BDD test case format was unavailable in version 7.5.6, which is the one I tested. However, the BDD test documentation is already in the app documentation. According to emails from TestRail support, the feature is being actively developed, and the option is already present for early-access users. Meanwhile, the rest of the users can use a "free-text" format for the BDD tests.

Zephyr Scale

Home Page:Zephyr Scale - Test Management for Jira

Version under the test: 9.10.0

Overview. Zephyr is probably the most popular test management tool, as it has native support for Jira. It has three different versions: Squad, Scale, and Entreprise. The difference between those versions can be a topic of a separate article, so I will mention that the Scale has the most balanced set of features that fulfill all my criteria. Also, I wouldn't say I liked the Squad. It's so limited that it is incomparable to the Zephyr Scale.

You can install Zephyr as a Jira app from the Jira marketplace and then enable Zephyr in the project settings. That's it. You are ready to start writing test cases and make test cycles and test runs with Zephyr default settings. Everything works buttery smooth out of the box. The design is minimalistic and super simple. You need to do a minimum of actions to create/run/manage tests. There is one feature related to testing sets that I actually didn't like. When you are working with test sets, you have the option to create folders and subfolders. However, when you select a folder, you will see test cases from the folder and all nested subfolders at once without grouping them by subfolders. So, you need to structure subfolders not and adequately keep test cases in the parent folder if you have any subfolders inside.

The API for test automation is good enough for integration with all popular automation tools. Now, onto the subject of pricing. It's free if up to 10 users are registered in your Jira instance. It costs 4.78 per user a month if you have more. It's way cheaper than TestRail.

Pros:

There is no need to install other plugins for Jira integration; all needed things are out of the box.
UX for tool usage is one of the best on the market.
Test tickets have their suffixes, e.g., (XXX-T1, XXX-T2), and by default, are shown only inside the Zephyr board or test runs.
Requirement management is out of the box.
A "Traceability" tab inside each test case allows you to quickly add a link to any Jira ticket (e.g., Epic, User Story, Task, or Bug).
Time tracking feature for single tests as well as for test runs.A good set of default reports and the option to make custom reports using JQL (advanced Jira queries).
Free for teams with up to 10 people

Cons:

Fewer things can be customized compared to the other competitors
Test data will consume Jira cloud storage
Not all of the keywords are supported for BDD tests

Xray for Jira

Home Page: Xray - Native Test Management for Jira

Version under the test: 6.3.2

Overview. Xray is commonly considered a highly customizable alternative to Zephyr. It allows you to create separate test-type tickets for tests, executions, and test plans to manage them the same way as with all other Jira tickets. It has a lot of customization options, but the learning curve is relatively high.

I had to read a lot of documentation and watch video demos to start working with the tool. You can set up everything for any development process, yet you must adjust many project settings. Also, it takes much more clicks to start a new test case compared with the other tools. Other than that, I had issues with slow-loading tests during the trial. However, the cached requests caused it, as everything started to work fine once I rebooted my router. I didn't have such issues with the other tools, and I hope the Xray team saw my feedback and has already fixed the problem. Pricing: it costs 10$ per month if you have up to 10 users and 4.25$ per user monthly if more users are registered in your Jira instance.

Pros:

Anything can be changed, added, or reordered.
Different types of test styles, including data-driven and fully supported BDD.
Advanced requirement traceability.
Advanced Reports / Document Composer. You can make almost any type of test document in any format you want.

Cons:

The test runs looked unusual to me.
There are no test runtime tracking options from the box. However, it can be done with a third party plugin.
The separate test-type tickets heavily spam the project, quickly increasing ticket numbers overall. As a Jira SuperAdmin, you must modify Backlog and Board query settings to hide the newly created test types, create a separate QA/QC project, and keep tests there.

AIO Tests

Home Page: All-In-One Test Management App for Jira

Version under the test: 3.1.6

Overview. AIO Tests is a Jira-native app with many installations from the Atlassian marketplace and good reviews from users. The tool has a one-click installation, and you are good to go with the app's default settings. Minimalistic but has an enjoyable interface. Everything looks logical to me. There are two types of test cases: classic and BDD. The UX for BDD cases is the best of the compared tools. The tests are created with test suffixes, the same as in Zephyr Scale, so they are not shown by default on the board or backlog. Another interesting thing is that the app doesn't allow you to add created test cases to the test sets and test cycles if they are not reviewed and approved(published). It forces you to follow the review process.

I would have preferred a better way to organize the test cycles. You can re-run tests inside a cycle several times, but only part of the cycle at a time. You can make a copy of the cycle as an option, but I used to use the cycles differently. However, the cycles in AIO Tests have a unique feature to execute them in offline mode. It's done by exporting the cycle into an excel file and then importing it back into the app.

Speaking about documentation, it's worth mentioning that it's pretty detailed and has many examples. I liked the chapter that explains how to make a beautiful dashboard with AIO widgets for thorough test process monitoring. Pricing: it's free for teams with up to 10 people but costs 350$ a month for teams with 10-50 people.

Pros:

It feels simple, but yet a powerful test management tool.
Good for BDD tests.
Free for teams with up to 10 people.

Cons:

It's pricey if you have a team with over ten people ($350/month).
Test cycles can't be re-run.
Integration with other tools is limited compared to the other competitors, but it still covers most of the popular tools and automation frameworks.

QADeputy

Home Page: QADeputy

Overview. I saw QADeputy mentioned on several websites as a good tool to try. However, I have yet to have a good experience with the tool. Jira integration allows you to see the test run status for the Jira side. There are no configuration settings at all. It looks like the company hasn't yet finished the Atlassian security self-assessment program, so the capabilities are limited. I briefly checked the test management stuff in the QADeputy instance to see what the app proposes. I wouldn't say I liked the UX, in all honesty. Whenever I tried to create a new test case or test set, I was stopped by a need to fill out some required fields that are commonly optional in most other tools by default. The BDD format is not yet supported—only free-text or step-based tests. The reports have just a few basic templates. Pricing: the tool is free for one user and costs 20$ per user/month (18$ per user in case of an annual plan).

Overall, the app needs to do much more to reach the level of other competitors mentioned in the article. The tool needs to meet my basement criteria. So, I will skip the cons and pros of this one. I hope it will improve and give it a try in a year or two with better results.

Conclusion

TestRail, Zephyr Scale, Xray, and AOI Tests are all pretty good test management tools. They have pros and cons, but any tool can be used for most projects. All of them have plenty of features needed for test management. They all have good support for the popular project management and automation tools, reports, import and export of test cases, and so on.

Speaking about my own experience and from Svitla Systems' expertise in Quality Assurance, I recommend TestRail as it still feels like a safe option. However, the UI for some of you might need to be updated. Also, if you are into BDD tests, I am sure that 'AIO Tests' is worth trying instead. Zephyr Scale is a viable alternative. It delivers the same things as TestRail and AIO Tests, with a bit different test management flow. The advantages are that it is free for small teams and not so pricy for larger teams.

Also, you can use Jira built-in things (JQL for the reports, labels, etc.) All 3 Jira-native apps (AIO, Zephyr, and Xray) will make the tests accessible for the whole development team, so developers can see which tests the QA team prepared for the features in advance.

Xray for Jira might be an option for you if:

you have a specific development process.
you have the full support of BDD format tests is a must-have.
you need to spend a bit more time on the app configuration.

As for me, QADeputy is not yet on the same level as the other competitors mentioned in the article. Still, I recommend you try it to form your own opinion.

I hope the article helped you choose a new test management tool for your projects.

May the quality be with you!

Interpreting Machine Learning models using LIME and SHAP (by Arturo Soberón, Senior Data Scientis at Svitla Systems)

Svitla Systems Inc. — Tue, 07 Feb 2023 13:09:15 +0000

You are seeing three images of a Husky dog lying on the snow. The image on the left side is incorrectly labeled as a wolf; the image in the middle has the snow cropped out but it’s correctly labeled as a dog; the image on the right has trees cropped out and the dog is once again mislabeled as a wolf instead of a dog.

Why interpreting models is important
When someone acts autonomously, it’s important to understand how and why they make decisions. How does a judge reach a decision when determining if someone’s guilty or not? How do doctors decide on a specific treatment? Why does a screenwriter decide to kill a main character at the end of a season?

In the near future, we can expect Artificial Intelligence (AI) models to take over decision-making tasks such as lawsuits, non-urgent patient care, or screenwriting. But before we get there, we need to thoroughly understand how and why a decision was reached. Unlike humans, AI models cannot be interviewed to understand the motives behind their decision-making processes, and even if we could interrogate them, we would be unable to hold them accountable for the decisions they made in the past. For these reasons, it is crucial to leverage methods that explain model predictions.

Let’s take banking for instance. Risk analysis uses AI models to assign credit scores to applicants and thus, decide whether or not to lend them money — applications with low scores are rejected and applications with high scores are accepted. These scores are generated by processing applicant data such as age, reported income, existing credits on record, and more.

Imagine if an applicant with a high score requests a very large sum of money. Bankers are known for playing it safe, and applications for large sums of money are likely to be thoroughly reviewed even if scores are high. Particularly, analysts are interested in knowing if the predicted scores from the AI model are a true reflection of the client’s ability and willingness to pay. They’re also interested in knowing if a vulnerability in the model or distortions in the data cause it to predict such a high score for the applicant. Within this context, understanding and explaining the decision-making process behind the AI model matters because there’s a lot of money on the line. Lending money to people who are unlikely to pay it back can have a serious and negative effect on financial institutions and the humans working for them.

AI models are used to predict outcomes in our everyday lives. In marketing, analysts employ user search history to recommend products to users that they are likely to buy; in the field of medicine, organ scans help predict the likelihood of a specific disease; in gaming, player attributes help match gamers with players of similar skill levels.

Regardless of the industry, analysts are interested in explaining how predictions are made by AI models to determine whether they can be trusted. The methods introduced in this article will help you disentangle the logic behind any model, whether it uses language, images, or tabular data to make predictions. Additionally, you will learn how to apply these methods using Python. The code presented in this article can also be found in this GitHub repository.

The trade-off between prediction accuracy and model interpretability
Generally speaking, complex models tend to make more accurate predictions than simpler models. However, understanding the logic behind a model’s predictions becomes increasingly difficult as we increase its complexity.

It’s easy to understand how a marginal increase in one of the input variables affects the predictions made by a linear regression model(1). Unfortunately, there are times when the relationship between the features and an outcome cannot be accurately predicted by a linear model, so we have to move on to more complex algorithms. Although the predictions made by, say, an ensemble of gradient boosted trees(2) could accurately model a nonlinear relationship, understanding why a prediction came out as it did is not as simple as before.

The accuracy gained by using an ensemble method is offset by the loss in interpretability of the new model. This is the essence of the infamous accuracy-interpretability trade-off.

What is LIME?
Not long ago, we used to think this trade-off was inevitable. Fortunately, researchers have proposed a few methods that can help us understand the logic behind the predictions made by any model, no matter how complex.

Local Interpretable Model-agnostic Explanations, also known as LIME, was proposed by Marco Tulio Ribeiro in 2016. As the name suggests, it is a procedure that uses local models to explain the predictions made by any model.

To use LIME, we need:

A model we can probe as many times as we need to; and
A model that has a predict_proba method (for compatibility with LIME).

LIME works under the assumption that the model you want to explain takes a set of features (such as a vector of numbers, a sentence, or an image) to make a prediction. It also assumes you can probe the model as often as needed. The procedure consists of fitting an interpretable model to a data set generated by slightly modifying a single observation.

The data set is created by taking all the features from a single observation and then iteratively removing some of its components to create a new set of “perturbed” observations. The model processes observations in the perturbed data set, and then, an interpretable model fits the latest data.

For example, imagine a Natural Language Processing (NLP) model that takes in a sentence and predicts its negative tone. LIME creates the perturbed data set using the procedure described above(3).

As shown in the table, removing the word “lazy” decreased the predicted negative score to zero, while all other perturbations had a negligible effect on the predictions made by the model. The idea is to fit an interpretable model (such as a decision tree or a linear regression) on the perturbed data set to understand which components influence the original predicted score most. The column “Similarity to original” is the share of words from the initial observation included in the perturbation and is used as weights to fit the simple model.

This example shows the procedure for an NLP model. In the case of tabular data, the perturbed data set is generated by slightly changing input vector values. In the case of images, chunks of the image (called “contiguous pixels”) are grayed out, and predictions are made on the remaining pixels.

As you may have noticed, following this procedure explains why the model made its prediction around the original set of inputs. In layman’s terms, this means that the explanation is valid for a single observation (i.e., a local explanation) and not for all possible observations (i.e., a global explanation).It is for this reason that the explanations made by LIME are considered to be local.

LIME in Python
We will now use LIME to explain the vaderSentiment model. First, install and import both modules:

For each text, Vader returns a dictionary with the text’s negative, positive, neutral and compound scores. Although LIME is model-agnostic (i.e., it will work on any model), it requires the model to be similar in structure to a sklearn object. In particular, the model needs to have a predict_proba method, so we will first modify Vader to make it resemble an sklearn instance.

NOTE: You can skip this step if your model already has a vectorized predict_proba method!

Let’s now make a prediction on the following text:
I HATE Mondays! I missed the bus and forgot my lunch…

If you’re in a notebook environment use explanation.show_in_notebook(). Otherwise, use explanation.as_pyplot_figure(). This is what the results look like in a Google Colab notebook:

Based on this output, you can tell the word that most influences the text’s negative score is HATE, followed by missed. The words that most heavily influence the text’s non-negative score are I and the.

What is SHAP?
SHAP was proposed in 2017 by Lundberg and Sun-In. Like LIME, it yields local explanations for predictions made by black-box models. The main difference is that SHAP uses game theory (a branch of Economics) to make local explanations.

SHAP stands for SHapley Additive exPlanations and is based on adding Shapley values together (Shapley values are solutions to cooperative games introduced by Lloyd Shapley in 1951). In short, a Shapley value represents the expected value of one player’s contribution in a game. To link game theory with machine learning, we must think of the model as the rules of the game and features as players that can either join the fun (i.e., the feature can be observed) or choose not to join (i.e., the feature cannot be observed).

To compute SHAP values, we need:

A model we can probe as many times as we need to;
A sample of observations; and
A very specific structure for the model (to be compatible with SHAP).

NOTE: With LIME, we needed one observation to generate an explanation. Because of how Shapley values are calculated, we will need a whole sample to explain a single observation using SHAP.

Suppose we have a fitted model f that uses input variables X = (X1, X2, …, Xp) to make a prediction, f(X). The goal of SHAP is to explain prediction f(X = x) by measuring how much each feature contributed to the model’s output at X = x.

SHAP in Python (linear regression example)
Calculating Shapley values is a messy process (it requires evaluating the model under many different combinations of input variables). Still, they are easy to visualize when dealing with an additive model (such as linear regression). For the sake of this introduction, imagine we are trying to find the most important variables of a linear regression model that predicts the severity of a patient’s diabetes one year after they were diagnosed.

We start by installing SHAP and importing all the dependencies we need to fit a linear regression to the diabetes data set.

Then, we fit a linear regression model to the data.

We will now draw a sample from the data (200 observations) to create a partial dependence plot for the Body Mass Index (BMI or also bmi for this article) feature. This step will help us visualize what Shapley values are and where they come from.

The vertical red line represents the difference between E[f(x) | bmi = 0.015] and the unconditional expected value E[f(x)]. This is the SHAP value for bmi when bmi = 0.015. Once we have calculated the SHAP values of all the remaining features, we can use the value represented by the red line to understand the importance of bmi in the 42nd observation.

We can explain the importance of each feature for the prediction made on the 42nd observation using a waterfall plot.

The above plot is read from the bottom up. The x-axis represents the predicted values for the target (the severity of the patient’s diabetes), and the y-axis represents each feature. We start at the unconditional expected value of the predictions made by the model in the sample (141.2). Each horizontal bar represents the SHAP value of each feature, sorted by magnitude in ascending order. This means that the least important feature for the 42nd prediction is age, followed by s4, followed by s3, and so on. Notably, the most important feature is s5, and it had a positive impact on the predicted severity of the patient’s disease. In this example, s5 represents the patient’s serum triglycerides level, which is known to increase the risk of strokes and heart disease.

As stated before, the plot starts at E[f(X)]. Note that the plot ends at f(x) = 171.3, the predicted value for the 42nd observation!

The fact that the waterfall plot starts at E[f(X)] and ends at E[f(x)] is not a coincidence. Rather, SHAP values are additive, and they always sum up to the difference between the prediction made on the observation we are trying to explain (E[f(x)]) and the unconditional expected value (E[f(X)]) (4). This is a desirable property because it allows us to interpret the sum of all SHAP values as the difference between the prediction when no features are present and the prediction when all features are present. In this context, each feature’s SHAP value represents its contribution toward the prediction made by the model.

Generally speaking, SHAP values cannot be seen in partial dependence plots when we are using nonlinear models (like neural networks). However, the intuition remains the same regardless of what model we are using: we explain the prediction made on a single observation by calculating the SHAP values of all features.

Key takeaways
AI models are used to predict our physical health, financial stability, willingness to watch a movie, and even our skill level in video games. These models are often allowed to act alone and make decisions autonomously. For this reason, explaining the logic behind models’ decision-making processes is crucial. However, we cannot sit down and interrogate an AI model, which is why methods to explain predictions are necessary.

LIME and SHAP can be used to make local explanations for any model. This means we can use either method to explain the predictions made by models that use text, images, or tabular data. On the one hand, LIME fits a simple model around a prediction to create a local explanation. On the other, SHAP uses game theory to measure the importance of each feature. In the end, both methods are used to understand how features affect a predicted outcome, though each method uses a slightly different procedure.

In practice, we can expect LIME and SHAP to yield similar results, so choosing which method to use ultimately comes down to personal preference. In any case, we can use both methods simultaneously to validate each other’s explanations. To summarize:

LIME generates a perturbed dataset to fit an explainable model, while SHAP requires an entire sample to calculate SHAP values. This means that LIME requires only one observation, while SHAP requires multiple observations.
SHAP values are relative to the average predicted value of the sample, which means that different samples will yield slightly different SHAP values.
Although LIME and SHAP are model-agnostic, both Python libraries require models to have a specific structure. LIME is slightly more flexible because it only requires a predict_proba method. Even though SHAP requires a more specific structure, the method works with the most popular AI libraries, including sklearn, xgboost, tensorflow and many others.

In Svitla, we have experienced Data Scientists and Machine Learning engineers with proven records of training and producing AI models. Our staff comes from a diverse background of computer scientists, engineers, mathematicians, and economists worldwide. We have successfully built end-to-end Artificial Intelligence solutions for some of the biggest tech, finance, and health companies. The work presented in this article is the culmination of many years of experience working with AI models and explaining their decision-making process for our clients to derive a clear understanding of the products and solutions we deliver. Reach out to one of our representatives so we can help you make the best decision for your project.

All the code used in this article can be found in this GitHub repository.

Words of caution

LIME or SHAP cannot replace performance metrics
The mean squared error, mean absolute error, area under the ROC curve, F1-score, accuracy, and other performance metrics evaluate a model’s goodness of fit. On the other hand, LIME and SHAP yield local explanations for a model’s predictions. In other words, these methods are not meant to account for the quality of a prediction, but rather, they tell us why a prediction was made.

For example, if you know that a given prediction is incorrect (i.e., a false negative), you can use either method to understand which components drove the model to classify that observation falsely. This will help you identify edge cases where your model performs poorly and can ultimately lead to better performance metrics (if you retrain your model, of course).

LIME or SHAP do not imply causality
Another important consideration to bear in mind is that your model is an approximation of the underlying data-generating process. Hence, explanations made by LIME or SHAP do not imply that those components have a causal effect on the target variable.

Explanations solely tell you why the model made a certain prediction. They do not necessarily imply that these relationships are reflected between the features and the target.

Linear regression is a simple learning algorithm that dates back to the 1800’s. It essentially finds the line that best fits a set of data to make predictions.
Tree-based gradient boosted ensembles are a series of decision trees (i.e., an ensemble) that are trained sequentially. Each tree in the ensemble focuses on the errors made by its predecessor to create an increasingly accurate model.
Natural Language Processing is a branch of Machine Learning that focuses on learning from text, voice and other forms of language-based communications.
Shapley values are based on the axioms of efficiency, symmetry, dummy and additivity. As a consequence, SHAP values are naturally additive. Winter (2022) offers a comprehensive review of the additive property of shapley values.

Communications in Remote Teams

Svitla Systems Inc. — Fri, 22 Apr 2022 12:33:18 +0000

At Svitla Systems, we know a thing or two about working remotely. For 19 years now, our employees have enjoyed the perks of a far more balanced lifestyle since we’ve encouraged remote work long before the pandemic struck.

While some organizations struggled, our rich expertise in remote work made it seamless to continue to provide ongoing, uninterrupted support for our clients, while keeping our employees safe and comfortable.

After many years of managing administrative processes for our remote employees scattered all over the world, we believe it’d be of great value to share firsthand experiences from our employees on how they’re coping with this new reality, what tools help them be more effective at their jobs even if working remotely, and some of the best practices they employ to ensure smooth and transparent communication with their teams.

Here, you’ll find real stories from Svitla Systems employees where they share their personal experiences, advice, and even some useful life hacks. Introducing our contributors, we’d like to say special thanks to Anna Bigun, Project Manager, Kostiantyn Bilyk, Team Lead and Lead iOS Software Engineer, and Dmytro Dehtiarov, Senior Software Engineer.

All three of them have convergent approaches to remote work that highlight how they’ve achieved a streamlined, transparent, and effective way of working remotely that leverages all the good bits of working from home while overcoming the challenges of missing out on face to face interactions.

Here we go!

How to manage a remote team communication

When managing communication in a remote team, one must always drive things home to what truly matters: trust and transparency. Almost all professionals understand that work is about being, well, professional, so it shouldn’t really be a problem for employers to allow employees to work from home either intermittently or always. Yet, that’s not the case still. Even with the pandemic, many executives were anxious about sending workforces home, as they feared they wouldn’t perform as well as they did if they were in an office.

Well, employees proved a lot of people wrong. In fact, studies show that working from home can increase productivity by 13%. A survey shows that 77% of people who work remotely at least a few times per month show increased productivity, with 30% doing more work in less time and 24% doing more work in the same period of time.

Other reports performed during the pandemic showed an increase in productivity by 47% since March of 2020 (compared to March and April 2019), and have deciphered when people are the most productive, which is on Tuesday, Wednesday, and Thursday between 10:30 am and 3:00 pm. Interesting, right?

With this context in mind, Anna Bigun, one of Svitla’s key Project Managers, shares the following insights about how to manage communication in remote teams successfully.

Consider time zones as they play a key role. When working with global teams, it’s important to factor time zones into your processes. If your team is all over the world, a person might not reply in your time zone, and that’s okay and to be expected. Thus, it’s critical that each member understands that team members are in different time zones and agree on common hours that are convenient for everyone to hold meetings. This rings true for clients as well. For example, it’s not uncommon to have a two-hour gap with team members working in the US as some are in Eastern Time while others are in Pacific Time. Now add Eastern European Standard Time to the mix and we got ourselves a lot of planning to do. Of course, you can almost always find that sweet spot where you all overlap even if it’s just for a couple of hours so you can communicate instantly, avoiding blockers by not having to wait for an answer just because you sent your email an hour late. This also helps boost efficiency, and productivity, maintaining a healthy work-life balance.

Choose your communication platform wisely. There are many tools and apps out there for you to communicate, whether it’s via email, Skype, Slack, or any other solution, the key is to carefully assess what will work best for the team and what will be convenient for everyone. This doesn’t mean that you have to commit to a single channel, but it is recommended that you keep it as narrow as possible because having too many options could lead to unwanted chaos and messy conversations. Keep in mind that structure is very important when working as a remote team. For example, Slack allows you to create theme-based channels and add people that are relevant. In contrast, Skype is not as friendly in that regard which is why it’s not as effective in most cases.

Rules and structure matter. Oftentimes, we see developers turn down a project because it requires too much communication. This is a downside of remote teams as remote work requires a lot of communication and calls. Thus, it’s very important to establish ground rules and regulations. For example, keeping an agenda of the questions that will be addressed during the call, the duration, and so on, goes a long way in keeping the call succinct and to the point. If time runs out, you can schedule a separate call but you respect everyone’s time for the first call.

The right methodology can be your best ally. If you are a Project Manager, you know it’s all about the methodology. Project Managers can advise clients about which methodology to use because some approaches work better for remote teams. It’s very valuable to select the right tracking tool to understand the team’s workflow and how everyone’s plate looks in terms of capacity management. Also, if some people don’t have any tasks assigned, you can plan and strategize what to do in such scenarios. When choosing the right methodology, don’t forget about documentation as it is a key element of all good projects.

Keep things social. People need people, that’s a fact. Some clients allocate budgets for team-building activities but this is not always possible. People tend to change their behavior when working online and offline, so it’s important to make the distinction and understand the context of any team gathering. For instance, online communication opens up the door to a lot of misunderstandings, so it’s recommended that you create a channel with the purpose of just communicating and having fun, leaving all work-related matters out of the conversation. As such, team members can share funny pictures, GIFs, personal news, or comment on anything they want as a means to relax, distract themselves in a good way, and strengthen team bonds.

How to manage technical processes and motivate the team

Of course, working from home comes with its challenges and it's important that organizations and individuals alike take these into consideration to be as effective as possible. For example, Stanford found that only 65% of Americans have internet fast enough to handle video calls, but the upside is that work-from-home employees are far less distracted, spending up to 30 minutes less talking about non-work topics.

One of the biggest aspects that have leaders and executives worried about is the lack of social interaction, a key factor in the success of any company. Some employees thrive on social interaction and struggle with being at home for this reason. In fact, never have we seen more organizations talking and worrying about employees’ mental health, as over time, it is bound to hinder productivity, employee satisfaction, and the overall wellbeing of the employee, which is, of course, the most important thing.

Here’s what Kostiantyn Bilyk, Team Lead and Lead iOS Software Engineer had to share about managing technical processes while simultaneously keeping the team engaged and motivated.

Hold regular, value-driven team meetings. Overall, the goal of a team is to be in constant communication to reach a common goal, and scheduling regular calls goes a long way in promptly identifying bottlenecks, giving solutions, and keeping everyone engaged in where the project is going. Through daily standup meetings, the team communicates what they’re working on, share their current workload, talk about issues they run into that are holding them back, bring up any questions, share knowledge, show results, and so much more, becoming a daily ritual where communication and knowledge transfer takes center stage.

Communicate the good and the bad. Sprint demo meetings are the place where teams show and highlight the results achieved. These meetings also serve as the stage to request feedback and talk about what was done right, what needs to be improved, and what will be done in the next sprint. Whether it’s a win or a feature that didn’t work out the way the team expected it to, there’s always a positive in all of it as the team either reinforces what they did right or takes key learning items to grow and be better. For example, developers often have meetings where the client is not invited so they can discuss internally how to best approach a feature, discuss an implementation issue, or bring up new ideas on how to develop something new. Either way, the purpose of these calls is to share knowledge and work on future tasks more assertively and effectively.

Keep the team informed. It’s good practice to keep a transparent, two-way street communication with team members so everyone feels connected, engaged, and part of the team. Even though everyone is most likely working on separate tasks, it is the sense of unity and belonging that moves a project forward. In some cases, Dev Leads hold meetings with the client once a week to discuss future tasks, share updates, and talk about the overall progress of the project. After these meetings, usually, Dev Leads brief the rest of the developers so they better understand where the project stands and where it’s headed.

Don’t treat people as employees, treat them as people. Building internal relationships with peers and colleagues is what ultimately builds engagement so everyone feels connected. Online chats about life and off-the-record topics (even memes) is a valuable start to break down barriers. Sometimes, it can be challenging to keep this type of communication going since teams mostly have interpersonal communication happen during different time zones, so it’s hard to coordinate everyone in one place at the same time.

Keep daily channels of communication. Slack has become an ally for remote teams all over the world. Teams typically have several channels open to address different topics. Before, calls were more effective, but now, it’s all about emails, instant messaging, and lightweight videoconferences.

Always account for the challenges of remote communication. One of the most challenging aspects of managing technical processes while keeping everyone engaged is to pick up on emotions. Sometimes, especially when team members are chatting, it is very complex to fully discern what a person is actually trying to say if they’re being overly polite, genuine, or sarcastic. Most prefer calls with camera on to this end so as to avoid any type of misunderstanding.

Respect everyone’s time and show empathy. Agree on specific time slots to hold meetings or schedule specific goals for a specific time so everybody knows what they should be working on and what to expect. Account for everyone’s time zones and be respectful of their time by keeping meetings strictly within the allotted time frame. It’s recommended that you keep a single channel of communication and schedule calls ahead of time and take everyone’s schedule into consideration, as much as possible.

How to be involved in communication in an effective way when you work in a big team

Overcoming communication challenges in remote teams can be challenging, but with the right team members, attitude, and tools, it becomes a breezy ride. Setting guidelines, using a standard communication tool, replacing in-person meetings with videoconferences, implementing a project management system, keeping communication channels open, giving feedback, and going that extra mile, are some of the best practices recommended to avoid most of the pitfalls of working remotely.

With the Covid-19 pandemic to consider, the shift to an online workplace is not only widespread but kind of a given in the IT industry. As many organizations are learning, managing communication flows in remote teams is a sinuous road with many twists and turns.

Keep in mind that human communication happens in bursts or periods of high activity followed by periods of little to no activity. These bursts of communication with periods of quiet in between are what make teams go round. Quiet times are used to develop ideas, focus, and carry on deep work while high-burst times focus energy, solve specific questions, and move teams to the next challenge ahead.

With this context in mind, it’s important to throw out the idea of message-based communication is asynchronous. Instead, team members should align routines and communicate in short time frames when everyone is there and can respond quickly, attentively, and effectively. That’s the secret sauce to higher performance.

These are the insights from Dmytro Dehtiarov Senior Software Engineer in regards to effective communication when working in a big team.

Learn to discern what matters. In big teams, there’s always a lot of noise across many different communication streams. To help this matter, it’s recommended that teams hold daily meetings to plan, discuss blockers, and discuss anything project-related with an open relationship with the client to discuss any concerns directly, with no middlemen involved.

Get your zen on. One of the most challenging aspects of remote team communication in big teams is technical issues. Sometimes people have slow connections, or the electricity is gone, so it’s difficult to keep the communication going when there’s a slow response time. Because team members can be in different time zones, it’s challenging to get answers in real-time, so it’s crucial that people learn to be okay with this and respect everyone’s unique circumstances.

Keep strict working hours. In the case of remote work, it’s easy to get carried away and work past your working hours. A late-night here and there never hurt anyone, but it’s important that you both respect your personal time and the personal time of others. In big teams, it’s recommended that everyone determines an overlapping time slot where almost everyone is online and available to improve response times.

Build a true partnership. Your peers and colleagues should be your partners and be there for you. If someone asks a question, the team is there for each other to search for or provide an answer, in true partnership form.

Conclusion

Remote teams are here to stay and will become the future of work. Over 40% of the global population is mobile, and highly-developed countries have an express ticket to reach 75% this year.

As entire workforces go remote, it’s critical that organizations shift to a new kind of management and leadership style that takes into account the unique challenges and benefits of working remotely across different time zones, languages, and cultural barriers.

While being spread out over the world can lead to teams feeling as if they weren’t truly a team, communication is chief in building a highly-collaborative, creative synergy amongst teams. Bridging that virtual gap can be challenging, but not impossible.

Svitla Systems has favored remote work for several years now (way before the pandemic) and is highly experienced in working with teams in different geographies, in a seamless and effective way. Our team of professionals is highly proficient at avoiding communication pitfalls and fostering strong collaboration ties with peers, partners, and clients alike.

Through a strong sense of team purpose, Svitla Systems professionals break down silos and communication barriers, with a clear sense of how their individual work fits into the bigger picture. For more information about how our team of highly-skilled and collaborative engineers can help your remote-based projects, reach out to one of our Svitla representatives and enhance your project’s communication in a virtual team setting.

Professional Benefits of Getting your AWS Certification (Dmytro Bavykin)

Svitla Systems Inc. — Mon, 28 Mar 2022 14:03:47 +0000

Greetings, everyone! My name is Dmytro Bavykin and I am a Senior Software Engineer at Svitla Systems.

For over 5 years, I’ve been working in the IT industry and I’m always in search of exciting, challenging, and interesting projects that help me level up my software engineering skills. Some time ago, I heard about the AWS certification from a few friends of mine and did a little research of my own to see which AWS certification should I get and if the program suited me so I could start to prepare for AWS certification.

Spoiler alert, I did get the AWS certification and it was a pretty good and thoughtful decision to make progress in my professional career.

In this post, I want to share my AWS certification path experience of choosing an AWS certification exam, preparing for it, the AWS certification costs that come with it, and how to make the best of the experience with quarantine protocols in full swing almost everywhere in the world.

What is an AWS certification?

The AWS certification is a credential that validates your knowledge, skills, and abilities on the cloud as deemed by the issuer, Amazon Web Services.

For me, the certification also sets the bar for cloud expertise and describes you as a well-disciplined, goal-oriented professional who wishes to learn new things about the cloud. If you’re hesitant about what to learn next, you can’t go wrong with AWS. But beware, some of you may never return to coding and decide to pursue CloudOps roles going forward.

What knowledge can you gain from a cloud AWS certification?

With several years under its wing, the AWS certification is
structured educational material set out to both instruct and determine how skilled you are in various areas of the cloud. The material is mostly geared to help you clean useful insights and skills that you could then apply in your day-to-day to make your engineering routine awesome.

For instance, you could optimize application performance, check its readiness for a disaster and improve availability, and much, much more, with everything you’ll get out of taking the AWS certification. Sometimes, you could save yourself a lot of headaches by understanding that the cloud has already solved your problem, leaving you to focus on end-product strategic tasks.

Before taking the AWS certification, I was completely unaware of how many unusual and interesting services AWS has to offer. Looking to create your own private blockchain, Ethereum-based network? You can use AWS Managed Blockchain. Tired of having to buy new iPhones to test your app with the latest versions of the device? AWS offers you Device Farm so you simulate different devices for testing purposes. Interested in sending 100 petabytes of data from your on-premises infrastructure to the cloud? You could request a Snowmobile! In all honesty, a Snowmobile is worth requesting just for fun :)

AWS equips your company with all the needed capabilities to become an AWS partner via the AWS Partner Network. AWS is interested in certified and trained individuals that can help them improve the customer experience.

Would it be better to try passing the AWS Certification Solution Architect or AWS Certification Developer Associate instead of Cloud Certified Practitioner (CCP) to save time and money?

I think it’d be helpful to put things in perspective. If you were new at a gym, would you go straight for the biggest weight you can find and try to lift it on your first visit? I would hope your trainer would stop you from even attempting such a thing on the first day because it could seriously injure you, and maybe even demoralize you. Well, the same logic applies to choosing the right AWS certification.

My recommendation is that you take things step by step and go easy on yourself. I believe what truly matters is the quality of your gained knowledge and that you give it some time and space to settle in.

If you start with one of the entry-level certifications, it would make it easier for you to take a higher-level certification as you would already be acquainted with the exam procedure and structure, the way questions are asked, and what you should focus on, giving you the confidence to know what to expect and be less anxious during next exams.

Now, let’s comprare the cost of AWS certification. The AWS Certification Associate Developer costs $150 at the time of writing the article. The AWS CCP certification costs $100. But, after passing the exam you would become a member of the AWS community which gives you access to some cool perks like discounts for future certifications.

For example, you could use your personal 50%, so it results in 100 + 75 = $175 USD. I think 25 dollars over is worth it, but everyone should choose the most applicable option for themselves.

Also, please consider that some companies compensate their employees for certifications they take that are within budget. Also, keep in mind that this is an investment in your future!

If you take any steps, big or small, it does not matter, the point is that you’re moving forward. Your brain is constantly exercised and you save your expertise from being overloaded or stagnant because if you’re not learning anything new, your knowledge slowly becomes outdated.

I hope my experience motivates you to start preparing for your AWS certification. Now, let’s discuss resources I found and used in preparation of the exam, which I hope will be helpful for you too!

Resources for preparing

Before officially taking the AWS certification, I had seen a similar course on sale at Udemy. The course seemed familiar because I had already taken part in a small learning group with friends as a means to incite some friendly competitive behavior and to prepare ourselves for the certification. I took the leap and signed up for the Udemy course prior to the actual AWS certification.

The most helpful and impactful course for me was imparted by Stepan Maarek → https://www.udemy.com/course/aws-certified-cloud-practitioner-new/, which includes one test exam which is pretty helpful.

Another thing that I would highly recommend is that you practice as much as possible. AWS Certification test exams helped me build an exam-oriented mindset, and here’s one you can use → https://www.udemy.com/course/practice-exams-aws-certified-cloud-practitioner/.

In total, I paid $20 which was a pretty good deal since regular price amounted to $80 for each. I also encourage you to go ahead and check out the course author’s LinkedIn page because they regularly gift discount coupons for their courses. Some authors offer a 30 day “no questions asked” money back guarantee, so why not go ahead and give it a go?

Be sure to leverage AWS resources. There is enough free guidance, instructions and videos in there to help you get started, study, and train → https://aws.amazon.com/certification/certified-cloud-practitioner/.

As for me, I checked a few free tests and watched a few recorded Twitch streams. I enjoyed the opportunity of watching the same material imparted by different mentors because sometimes you understand new information better from another speaker.

I had some practice on AWS. AWS offers a 1-year free tier if you are a new user. Free tier does not cover every service, but you will have some free quotas for a lot of services. You can read more about it here. I’ve had my account for years, so I didn't have free tier while preparing, and after using a few services, my bill amounted to only $0.13 in total.

If you wanna shadow my steps, I did it in such order

Watch a Udemy course as training for AWS certification
Try to pass the test exam inside the Udemy course
Review your exam and replay the parts you failed during the exam
Complete the test exams from the practice set
Go back again to review your exam and replay the parts you had a hard time addressing during the exam
Strive for an average score in all tests of at least 85% because the certification requires at least 70% to pass
Explore the free AWS resources and check current rules of passing the certification. Rules are shown in the course, but I found a difference between what was shown and what actually took place. The exam consists of 65 questions, but only 50 of those questions affect your score, and 15 questions are used to collect data for AWS needs. You can learn more about it here.

How to register for your first AWS certification

When you are ready enough, the next step is to register for the actual certification. You need an Amazon account for it, not an AWS account →https://www.aws.training/certification.

Once registered, you have two options: To take the certification online or offline due to COVID. In my case, I did not want to wait 2 weeks for the closest available offline date, so I chose Pearson VUE online. I suggest that you read the rules of passing the online certification very carefully at least twice as some rules contradict themselves. Keep in mind that your working space can’t have anything that could consist of information. Food, wearable devices, headphones—also forbidden.

Check the documents you need to have with you to verify your identity, make a system test of your laptop/PC, and ensure you’re the only person in the room during the entire certification. Keep in mind that your PC can be tracked to check you are not cheating.

Thirty minutes before the exam, I started the check-in process, made photos of my identity documents, and of my working space from different angles. Next, I ran a system check to check sound, video, bandwidth, and running processes. After having gone through all those checks, I was put on queue. My proctor, who would be watching my entire certification, joined and asked me to show my working space using a webcam. Then, I was politely asked to show my hands and wrists, and I’ve heard that people with long hair are sometimes needed to show their ears.

And so, the journey began. After answering a few questions, I had a chance to fail and my exam might be revoked. I got a warning from my proctor for MUMBLING. I think I was too concentrated and started moving my lips while reading questions. I suppose it could look suspicious. I did not try to argue, I said sorry to my proctor, and pursed my lips.

You have 100 minutes to pass the certification and I used 40, so I had plenty of time to review my questions, but I had a gut feeling that everything was fine and just clicked “Finish”. You can see the overall status of whether you passed or fail, and then it takes up to 5 business days to send you a full report. I got it on the next day. You need 700 points from 1000 to pass the exam, and the exam evaluates between 100 to 1000. My score was 882, which I was pretty satisfied with. Last but not least, after receiving a full report of your score, you can link your AWS badge to your LinkedIn account and start preparing for the next level.

Conclusion

It took me about 6 weeks from buying the first Udemy course to actually adding the AWS certification to my LinkedIn account. In my first Udemy course, I failed with a 69% score, but I kept reading, studying, taking test exams, and learning more and more from different sources until my score was 90% in my trials

I suggest that you take it easy on yourself and don’t look for perfection. Prepare yourself to gain confidence as even people with vast cloud experience can make mistakes at an exam.

I spent $20 in Udemy courses, $100 for the AWS CCP registration, and 0.13$ for AWS services I used while preparing, so in total, it was $120.13 dollars for me. Now, I feel more confident about my AWS knowledge and have a weird urge to fit every one of my needs with one of the solutions that AWS offers. What’s next? I think there’s little time for hesitation, so that’s why I already started preparing for my AWS Developer Associate certification.

Tips for Startup Founders: Checklist for Hiring Offshore Developers

Svitla Systems Inc. — Wed, 08 Dec 2021 16:38:43 +0000

Successful startups are built on strong, common ground: talented resources who help shape, build and transform a business idea into a reality. These resources can range from engineers, software developers, project managers, testers, business analysts, data scientists, and more, all of whom can be based anywhere in the world.

After the challenges the IT industry, and every industry for that matter, faced in 2020, many organizations were prompted to adopt and embrace new ways of staffing their projects. With the entire world going remote, it began to make even greater sense to hire offshoring resources that could provide services with no ties to a physical location.

Offshoring is no longer a trend, but a legitimate way of generating more business opportunities and value without breaking the bank. This is especially attractive for startup operations as budgets can be tight and it’s important to make every dollar count, especially in the early stages of your startup company.

On top of costs, it’s also crucial to hire top-tier talent that can meet the modern business demands of the industry along with information technology trends. It’s safe to say that it’s virtually impossible to find all the talent your startup needs in a single location and at good value; talent is everywhere, so why should you limit your startup to geographic barriers?

In this blog post, we’ll be unpacking how startup organizations can not only benefit from hiring offshore resources, but thrive by hiring the best available resources no matter where they’re located, giving a competitive edge in terms of support, costs, risk, and skills.

Let’s dig right in!

What is Offshore Software Development?

Aspiring startups know that skilled software engineers are crucial to the success of their product or service, yet it can be really hard to find top-tier software engineers that are available or open to new opportunities.

Outsourcing is the answer for companies looking to tap into the best tech talent budgets can afford, giving them an upper hand to streamline the software development lifecycle with resources that are already very knowledgeable on the subject and that have a variety of projects under their belt.

Without embellishments, offshore software development consists of moving the software development process of your software projects to a country where production costs are lower.

Now, for a little trip down memory lane, let’s take it back to the 1980s where Accenture, formerly known as Andersen, which was popular for taking unstructured system requirements from clients and turning them into a working system for millions of dollars. While the term outsourcing wasn’t exactly used for this practice, Andersen was known for training consultants in software development similar to what today is known as the Waterfall methodology.

Fast forward to the mid and late 1900s where India-based firms began to take projects offering programmers, system analysts, and project managers for a fraction of what US firms were charging. While they weren’t taking a bite of the large-sized projects yet, Indian firms began to set a precedent as highly-skilled, highly available resources. As the year 2000 was getting closer, and lots of companies were getting their systems ready for Y2K, offshore resources became the answer for many looking to get better costs.

Twenty one years later and things have evolved greatly. Now, project-based outsourced software development is more Agile than Waterfall, and India, while still one of the strongest countries to offer offshore software development solutions, is no longer the only country that offers these types of services as many others are beginning to earn a well-deserved reputation for reliable software outsourcing destinations. Eastern European countries like Ukraine, Poland, or Belarus are among the top outsourcing destinations. Latin American countries like Mexico, Brazil, and Chile also house large concentrations of software developers and are increasingly becoming a hot spot for outsourcing. In Asia, China and the Philippines are right behind India as the top countries that offer professional developers.

Comparison of Offshore and In-house Software Development

The decision to turn to offshore solutions for software development mainly stems from wanting to lower costs, as many companies are faced with constantly dealing with budgetary constraints. Building a software project from the ground up can be an expensive endeavor, so offshore resources are often the best path to follow.

Choosing between investing in an in-house team of software developers or outsourcing software development to an offshore company is based on several criteria like budget, complexity of the project, and time to market, to name a few. In-house teams of engineers work in the employer’s facilities, making it a seamless workflow that offers a greater perceived control over project customizations, giving organizations direct control over picking and choosing the required skill levels that engineers must possess for a unique project.

Offshore software development is a no-brainer if you’re looking to hire a wide range of expertise in software development tasks at a fraction of what you’d typically pay if hiring everything in-house. Offshore software development centers offer virtual services and remote employment so clients benefit from uninterrupted support and a streamlined process with their team extensions. Offshore software development allows companies to tap into many different areas of building a software product or service such as artificial intelligence, machine learning, data science, etc., all of which can be hard and expensive to find in a single geographic location.

If your organization chooses to go with the in-house team direction, you need to be prepared to pay for a premium as in-house teams are more expensive to build and maintain. Oftentimes, companies look at in-house teams as a means to have similar language and cultural background, thus speeding up software development but with how interconnected the world is nowadays, this can be virtually achieved anywhere in the world. While offshore software development centers are typically in non-English speaking countries, professionals almost always speak the language fluently to communicate effectively with clients.

Here’s a comparison of offshore and in-house software development across several areas:

Costs. It’s a lot more expensive to recruit, train, hire, onboard, and grow an in-house team of developers than offshore software development resources.
Breadth of specialized skills. Hiring in-house engineers can prove challenging when you’re talking about different skill sets needed for a project. For instance, Data Scientists or an engineer specialized in a specific programming language are not that easy to come by and it may be the case that you don’t find a suitable candidate in your region, which is not a problem in offshore centers where they can easily access their pool of resources across the globe and hand clients the right professional for the specified task.
Communication. Many believe that in-house teams possess inherently better communication because they have direct access to other owners of the software development process. While this may be the case, it’s also true that the pandemic truly broke down a lot of barriers in the regard that nearly everyone went remote, so virtual communication became the norm which is something that offshore software developers were already experts at, making it easier to communicate with clients, teams, stakeholders, etc.
Timezone and language. While in-house teams seem to have the upper hand in this area, it’s also worth mentioning that offshore developers give clients the ability to offer support around the clock. Also, as stated earlier, the current health crisis pushed in-house employees to go remote as well, putting everyone in the same square when it comes to communication.

Advantages of Offshore Development for Startups

Every startup is different, but they’re all in search of the best talent. When weighing the pros and cons of accessing the talent pool of offshore software development, startups have a lot to consider. This process can be easier by doing research, reviewing portfolios, or reading case studies to see what best practices apply to your unique situation. After looking at the hard facts, it won’t be difficult to see the many benefits you’ll gain by engaging with a software development company, mainly in the budget department.

Here are some of the top advantages of working with an offshore software development company for your startup:

Money. Startup companies are typically looking for every way possible to lower costs and hiring an in-house team of software developers can be a luxury that you simply can’t afford at this stage of your company. By hiring an offshore software development company, not only do you significantly lower the costs of developing your product or service, you also access a pool of resources that would otherwise be virtually impossible to achieve if you were to build an in-house team of developers.
Performance. Offshore software developers work with lots of projects that give them exposure to new technologies, methodologies, ways of doing business. All of this adds to their skill set, making it easier to perform better and improve your project’s efficiency. When choosing a competent vendor, you can get a holistic software development approach to your project that includes consulting, analytics, testing, UI/UX design, project management, and any other service your startup will inevitably need.
Flexibility. Startups thrive on flexibility, so it’s important to have a team of developers that easily adapt and respond to changing business and market demands. If you were to have an in-house team, it’s traditionally more time consuming to ensure everyone aligns to a specific goal or find the right candidates that can meet the needs of a specific technology. By putting the software development tasks in the skilled hands of the right offshore team can significantly free up your time so you focus on other vital areas of your startup like Marketing, Financials, searching for investors, go-to-market strategies, and more. What’s more, offshore software development teams offer reliable and dedicated professionals who have proven expertise and knowledge on unique technical areas that can make your product or service more effective, all within an agreed-upon deadline.

Checklist for Hiring an Offshore Development Team

Okay, so you’ve decided that what’s best for your company is to go with an offshore development team...but how do you choose the best one? Worry not! We’ve prepared a handy checklist that you can use as the basis of your selection process to land a quality vendor who will take your project and needs, and make them a priority the way you need them to.

Build a robust, specific job description of the type of professionals that you need for your software development project. By delineating specific parameters, it’ll be a lot easier to find vendors that have professionals who fit the bill.
Perform background checks for legitimacy.
Look at the vendor’s track record and industry reputation. See how many clients they’ve worked with, the size of the clients they’ve worked with, and the type of culture they promote. Are employees happy? Employee satisfaction says a lot about the success and wellbeing of a software development vendor.
Ask about similar projects and how long it took them to deploy successfully.
Read case studies of similar startups to your own to see the vendor they used and why it worked for them. By looking at other clients’ real data, you’ll get a clearer picture of the type of vendor that can suit your startup’s needs.
Compare prices between at least 2-3 vendors to get an idea of the average pay. Consider that specialized roles will incur in slightly higher salaries, so be mindful of the project requirements so the vendor can assign the right roles to your startup.
Ask about the vendor’s style of work, methodologies they most often employ, and some of the latest technologies they’ve worked with.
Confirm the typical cadence to show MVPs, prototypes, or demos so you get an idea of what to expect for your project.
As offshore teams are an extension of your team, confirm with vendors that you’ll hold total control over development.
Confirm the type of communication channels they use to guide a project to completion.

Freelance Platform or Outsourcing Software Development Company?

As of late, hiring software developers under a freelance model is gaining popularity. Without the legal ties of a contract to a major outsourcing software developer, startups are tempted to go with the freelancing route but it can easily backfire if not done properly as it’s more complex to confirm the legitimacy and reputation of a freelancer.

While it’s true that software developers of offshore companies can cost a little bit more than freelancers, it’s also true that freelancers can’t guarantee their work in your project or maintenance after project completion. In short, outsourcing software developers give you a significant and tangible upper hand in terms of security.

Freelancers, thanks to their self-employed nature, manage their time based on the amount of projects they perceive they can undertake at a given point in time. When choosing to outsource software developers, the vendor has a management team that carefully assigns resources to a project based on their skills, expertise, and availability, so as not to overload them and give the client a satisfying end result.

What’s also worth mentioning is that in an outsourcing model, it’s easier for developers to access numerous resources and technologies, making it easier and more effective to guarantee high-performing software products or services, which is not the case with freelancers. Freelancers rely on their own equipment to get the job done and are best suited for small, specialized tasks rather than a full-fledged project like a startup.