Webinar|May 20, 2021

Introduction to Azure Databricks

Data volumes are increasing rapidly and with it, insights can be gained at cloud scales. Azure Databricks enables developers to leverage the combined power of Azure, Delta Lake, and Apache Spark to utilize structured and unstructured data for insights, real-time feedback, and application development. In this session, you’ll learn the basics of Azure Databricks, the…
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AI|May 19, 2021

ML & AI for Software Developers - Part 5
Regression Modeling

When you build a machine-learning model, the first and most important decision you make is what learning algorithm to use to fit the model to the training data. In my previous post, I introduced some of the most widely used learning algorithms for regression models: linear regression, decision trees, random forests, gradient-boosting machines (GBMs), and…
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Webinar|May 13, 2021

Making the Most of Microservices

Microservices offer a way to create hyper scale applications that can meet the demands of even the most challenging workloads. But with microservices, come with their own sets of challenges that have to be solved. In this webinar, we look at these challenges: state, configuration management, service discovery, interservice communication, security, and scaling data. With…
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Azure and Cloud Computing|May 7, 2021

Azure Migrations - Part 2
Starting an Assessment with Discovery

The first critical step in migrating anything to Azure is an assessment of what we want to move. The assessment starts with a discovery wherein various aspects of a workload are teased out to expose what composes and supports the workload. These include business drivers, governance drivers, security, and the actual workload itself. In this…
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AI|May 6, 2021

ML & AI for Software Developers - Part 4
Regression Algorithms

Supervised-learning models come in two varieties: regression models and classification models. Regression models predict numeric outcomes, such as the price of a car. Classification models predict classes, such as the breed of a dog in a photo. When you build a machine-learning model, the first and most important decision you make is what learning algorithm…
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Azure and Cloud Computing|Apr 30, 2021

Azure Migrations - Part 1
Learning Azure

This is the first video in a multi-part series on Azure migrations by Wintellect Architect and Azure MVP Blaize Stewart. In part 1, we will look at an overview of what an Azure migration looks like, including learning, assessing, planning, and executing a migration into Azure. We will then review topics that will help you…
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AI|Apr 29, 2021

ML & AI for Software Developers - Part 3
Supervised Learning with k-Nearest Neighbors

Most machine-learning models fall into one of two categories. Supervised-learning models make predictions. For example, they predict whether a credit-card transaction is fraudulent or a flight will arrive on time. Unsupervised-learning models don’t make predictions; they provide insights into existing data. The previous post in this series introduced unsupervised learning and used a popular algorithm…
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Webinar|Apr 28, 2021

Power BI Aggregations

Power BI was originally released as a fully cloud-based, end-user targeted data visualization tool. Now it’s both a robust and scalable enterprise-level BI ecosystem. Microsoft has increased its ability to deal with a large volume of data both imported into and hosted outside the service to implement semantic models. In this session, Wintellect Data Architect…
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Webinar|Apr 22, 2021

Making Sense of the Power BI Ecosystem

Power BI adoption growth has been astonishing. And with growing demand, we also have growing needs. The service started as an end-user-driven data visualization tool but is now at the enterprise-grade semantic layer. In this session, you will learn more about the major Power BI features and in which use cases to apply them. What…
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AI|Apr 19, 2021

ML & AI for Software Developers - Part 2
Unsupervised Learning with k-Means Clustering

Machine-learning models fall into two broad categories: supervised-learning models and unsupervised-learning models. The purpose of supervised learning is to make predictions. The purpose of unsupervised learning is to glean insights from existing data. One example of unsupervised learning is examining data regarding products purchased from your company and the customers who purchased them to determine…
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Webinar|Apr 1, 2021

Building and Managing Containers with Azure

Building and managing containers are a large part of any container deployment. The entire process requires multiple steps and different tools to complete. Azure provides several different ways to manage automation and container images for deployment to container platforms. In this session, you’ll get a glimpse of three different ways to do this with Azure…
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