AI|Jun 22, 2021

ML & AI for Software Developers - Part 8
Binary Classification: Spam Filtering

My previous post introduced a machine-learning model that used logistic regression to predict whether text input to it expresses positive or negative sentiment. We used the probability that the text expresses positive sentiment as a sentiment score, and saw that expressions such as “The long lines and poor customer service really turned me off” score…
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AI|May 27, 2021

ML & AI for Software Developers - Part 6
Binary Classification

The machine-learning model featured in my previous post was a regression model that predicted taxi fares based on distance traveled, the day of the week, and the time of day. Now it’s time to tackle classification models, which predict categorical outcomes such as what type of flower a set of measurements represent or whether a…
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Blogs|May 21, 2021

Azure Migrations - Part 3
App Services Migration Assistant Assessment Report

Azure App Services is a popular choice for migrating apps to Azure because it gets users out of the business of managing servers. But with App Services, some limitations might mean reconfiguring or refactoring an application. The Migration Assistant utility can provide a quick report that shows you things you need to consider before moving…
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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 12, 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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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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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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Blogs|Apr 16, 2021

Machine Learning and AI for Software Developers – Part I

Machine learning (ML) and artificial intelligence (AI) are transforming the way software is written and, more importantly, what software is capable of. Developers are accustomed to writing code that solves problems algorithmically. It’s not difficult to write an app that hashes a password or queries a database. It’s another proposition altogether to write code that…
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