DEV Community: Mark B

Powering Smart City Solutions with AWS IoT & GovCloud (US) - Webinar with Ubicquia and AWS

Mark B — Thu, 19 Jan 2023 06:32:41 +0000

Smart city and streetlight IoT platform helps cost savings with migration of 100+ servers and 1000s of databases to AWS.

This webinar will be beneficial to those who are:

Planning to build or building Smart City applications on the cloud
Building Multi-tenant, complex data platform on AWS
Building a Highly scalable IoT streaming data platform to process millions of transactions per day
Looking for cost savings by leveraging AWS Cloud, cloud-native services, and data components
Building a secure data platform on Cloud which comply with FedRamp HIGH baseline standards

Register Here: Powering Smart City Solutions with AWS IoT

Webinar: Powering Smart City Solutions with AWS IoT & GovCloud(US)

Mark B — Wed, 11 Jan 2023 13:26:41 +0000

Learn how Anblicks helped Ubicquia in building a smart city and street light IoT platforms and helped in saving cost by migrating 400+ servers and 1000s of databases to AWS.

Date & Time: 25th January 2025, 11:00 am (CST)

Anblicks will host a webinar “Powering Smart City IoT Solutions on AWS & GovCloud,” on Jan 25, 2023. The webinar will be presented by Gregory Walters (Director – Software Engineering, Ubicquia), Ronald Smith (VP-Software Development, Ubicquia), Jeff Friedman (Business Development Manager, AWS), Rahul Kochhar (IoT Architect, AWS), and Jayesh Prajapati (Director-Delivery, Anblicks).

This webinar will be beneficial to those who are:

Planning to build or building Smart City applications on the cloud
Building Multi-tenant, complex data platform on AWS Building a Highly scalable IoT streaming data platform to process millions of transactions per day
Looking for cost savings by leveraging AWS Cloud, cloud-native services, and data components
Building a secure data platform on Cloud which comply with FedRamp HIGH baseline standards

Powering Smart City Solutions with AWS IoT & GovCloud(US)

Mark B — Mon, 09 Jan 2023 09:47:45 +0000

Learn how Anblicks helped Ubicquia in building a smart city and street light IoT platforms and helped in saving cost by migrating 400+ servers and 1000s of databases to AWS.

Register here: Powering Smart City Solutions with AWS IoT & GovCloud

Want to implement Snowflake and migrate legacy data warehouse to the cloud?

Mark B — Wed, 16 Nov 2022 12:56:00 +0000

As a Snowflake consulting partner, Anblicks migrates data from legacy systems to the Snowflake data cloud on time and within budget, enabling instant scalability and availability of your data and analytics assets. More information. Snowflake Consulting Services & Implementation Partner USA

Top 10 Small-Scale to Enterprise-Level Cloud & Open Source ML Platforms

Mark B — Mon, 14 Nov 2022 13:16:26 +0000

Businesses continue transforming their operations to increase productivity and deliver memorable consumer experiences. This digital transition accelerates timeframes for interactions, transactions, and decisions. Additionally, it generates reams of data with brand-new insights into operations, clients, and competition. Machine learning helps companies in harnessing this data to gain a competitive advantage. ML (Machine Learning) models can detect patterns in massive amounts of data, allowing them to make faster, more accurate decisions on a larger scale than humans could. This enables humans and applications to take quick and intelligent action.

As more businesses experiment with data, they realize that developing a machine learning (ML) model is only one of many steps in the ML lifecycle.

What is Machine Learning Lifecycle?

The machine learning lifecycle is developing, deploying, and maintaining a machine learning model for a particular application. The typical lifecycle includes:

Establish a business objective
The first step in the process starts with determining the business objective of implementing a machine learning model. For instance, a business objective for a lending firm can be predicting credit risk in a certain number of loan applications.

Data Gathering & Annotation
The next stage in the machine learning life cycle is data collection and preparation, guided by the defined business goal. This is usually the longest stage in the development process.

Developers will select data sets for the model’s training and testing based on the type of machine learning model. Take credit risk as an example. If the lender wants to gather information from scanned documents, they can use an image recognition model; for data analysis, It would be snippets of numerical or text data gathered from loan applicants.

The most crucial stage after data collection is annotation “wrangling.” Modern AI (Artificial Intelligence) models require highly specific data analysis and instructions. Annotation helps developers increase consistency and accuracy while minimizing biases to avoid malfunction after deployment.

Model Development & Training
The building process is the most code-intensive element of the machine learning life cycle. This stage will be mostly managed by the development team’s programmers, who will design and assemble the algorithm effectively.

However, developers must constantly check things during the training process. It is critical to detect any underlying biases in the training data as quickly as possible. Assume the image model cannot recognize documents, forcing it to misclassify them. In this situation, the parameters should instruct the model to focus on patterns in the image rather than pixels.

Test & Validate Model
The model should be completely functional and running as planned by the testing phase. A separate validation dataset is used for evaluation during training. The goal is to see how the model reacts to data it has never seen before.

Model Deployment
It is finally time to deploy the machine learning model after training. At this point, the development team has done everything possible to ensure that the model functions optimally. The model can operate with uncurated low latency data from real users and is trusted to assess it accurately.

Returning to the credit risk scenario, the model should reliably anticipate loan defaulters. The developers should be satisfied that the model will meet the lending firms’ expectations and perform properly.

Model Monitoring
The model’s performance is tracked after deployment to ensure it keeps up over time. For instance, if a machine learning model for loan default prediction was not regularly refined, it could not detect a new default type. It is critical to monitor the models to detect and correct bugs. Any key findings from the monitoring can be used to improve the model’s performance.

Continue reading: Top 10 MLOps Tools/Platforms for Machine Learning Lifecycle Management

How to Migrate from on-prem data environment SSIS to ADF in Just 12 Weeks

Mark B — Wed, 26 Oct 2022 14:02:01 +0000

As the data in your company grows rapidly, there could be bottlenecks in processing and delivering business reports within the service level agreements on the current on-premises systems since they are not easily scalable. Additionally, there could be issues with increasing costs and the ability to adapt to modern data architectures involving data lake/lake house, etc., that could help drive business insights from big data.

Know more about the service: Migrate from SSIS/SSRS/SQL Server on premise to Azure

4 Step Strategy to Migrate SQL Server to Azure Cloud

Mark B — Wed, 19 Oct 2022 11:29:05 +0000

Microsoft Azure is the world’s second-largest cloud computing provider, delivering a wide array of services, including storage, computing, networking, and analytics. Azure delivers tools that assist you efficiently and quickly migrating on-premises resources, including databases, to the Azure cloud.

You can migrate on-prem databases driving various database engines, including MySQL, PostgreSQL, and Microsoft SQL Server, to the Azure cloud. Azure migration helps with numerous methods, including importing and exporting database content into a consistent database on Azure, supporting databases and reforming them on Azure, documenting custom migration code, or utilizing automated Azure Database Migration Service (DMS).

Azure delivers three main circumstances to execute SQL Server in Azure, including:

IaaS on Azure – install & execute the database on Azure virtual machines (VMs).
PaaS on Azure – migrate the on-prem database to Azure SQL Database.
IaaS and PaaS – utilize Azure SQL Database managed instances to gain IaaS and PaaS features.

Azure offers free, automated tools that assist you in performing Azure migration for on-premises SQL Server databases, using any of these above deployment models.

SQL Migration to Azure: 4 Main Strategies - Read on

A Brief History & Evolution of Big Data Analytics [Infographic]

Mark B — Tue, 18 Oct 2022 13:59:02 +0000

It is really obvious that big data is absolutely providing substantial value to users. Larger businesses are driving the way and obtaining results from big data with focused initiatives. Big Data Analytics Solutions have the possibility to be not just disruptive, but also potentially transformational. The agreement is clear: big data brings interruption that revolutionizes business. Thriving big data organizations are leveraging big data tools and big data technologies to drive results across immense, complex businesses in many diverse industries.

The big data ecosystem is continuously changing with emerging technologies. As a result, it is very essential to analyze a broad range of technology options before moving big data strategy with an emphasis on outcomes that can operate as critical business differentiators.

Stay agile and be ready to adopt changes, adjust, and understand as technologies evolve and new opportunities can be analyzed.

This infographic provides details about the History of Big Data Analytics, Enterprise Tools and Technologies, Challenges and Benefits as well as Trends that will define the future.

A Brief History of Big Data: Read more

What is Azure Data Factory? How does ADF work?

Mark B — Fri, 14 Oct 2022 10:42:50 +0000

Azure Data Factory is a Microsoft cloud service offering that provides data integration from various sources. It is part of the Azure platform. ADF is a great option for creating hybrid extract-transform-load (ETL), extract-load-transform (ELT), and data integration pipelines. In simple terms, an ETL tool collects data from various sources, transforms it into useful information, and transfers it to destinations such as data lakes, data warehouses, etc.

How does ADF work?

Combine and connect: Gather and combine data from various sources. The data can be structured, semi-structured, or unstructured.

Centralize and store: Transfer and store data from on-premises storage to a centralized location, such as a cloud-based store.

Transform and analyze: After storing data in centralized cloud storage, use computing services such as HDInsight Hadoop, Spark, Data Lake Analytics, and Machine Learning to process or transform the data collected.

Publish: After refining the data and converting it into consumable form, publish it to cloud stores like Azure Data Lake, Azure Datawarehouse, and Azure Cosmos DB, whichever analytics engine your business users can point from to their BI apps.

Visualize and Monitor: For further analysis, visualize the output data using third-party apps like Tableau, Microsoft Power BI, Sisense, etc.

How to Connect Azure Data Factory (ADF) with Azure DevOps

Mark B — Thu, 13 Oct 2022 12:34:27 +0000

MLOps vs DevOps: What's the Difference?

Mark B — Thu, 29 Sep 2022 17:45:48 +0000

DevOps
DevOps is a practice where people work in a team to build and deliver software at the best possible speed. DevOps enable software developers(devs) and operations(Ops) teams to fasten up the delivery of Software through collaboration, and in an iterative manner.

MLOps
DevOps is for streamlining software development and then deploying and monitoring them. In MLOps we focus on Machine Learning Operations. So, the guys who are involved in this methodology are data scientists, IT, and DevOps Engineers. It is a useful approach for creating best-in-class machine learning solutions for the end-user.

Which is better, Azure Data Factory or SSIS?

Mark B — Thu, 22 Sep 2022 08:23:27 +0000

Which is better, Azure Data Factory or SSIS?

SSIS is an ETL tool for on-premises use that may also be used for ELT. ADF is a cloud-based solution for constructing ELT data pipelines. Implementing ETL use cases in ADF is also feasible using the data flow features

Experts recommend using Azure Data Factory as your preferred choice for a data movement orchestration tool. If you’re happy with your present SQL Server Integration Services workloads, remember that you can still run them with Azure Data Factory.