Volume 35 Number 7
Mark Michaelis this month delves into .NET 5, the promised universal framework that unites the parallel threads of .NET Framework, Xamarin/Mono, and .NET Core into a single, universal target for desktop, Web, cloud, and device developers.
[The Working Programmer]
Ted Neward closes out his series on the Naked Objects Framework with a look at NOF Restful API networking, and how the entire state of the network interaction is stored in a hypermedia document shared between the client and the server.
Artificial intelligence and machine learning services are more accessible than ever, yet developers and data scientists face the growing challenge of choosing among them. In this hands-on exploration, Ashish Sahu walks through an end-to-end AI scenario and explores how the various Microsoft Azure AI services can be applied at each stage.
[ASP.NET Core 3.0]
Stefano Tempesta continues his exploration of a biometric security system, as he walks through collecting facial information from cameras registered as IoT devices and streaming data to an IoT Hub in Azure. The solution leverages a machine learning service that analyzes each access request against a historical dataset to thwart unauthorized intrusions.
Microsoft ML.NET is a large, open source library of machine learning functions that lets you create a prediction model using a C# language program. Writing such a program isn’t simple, but as James McCaffrey explains, the AutoML system uses the ML.NET command-line interface (CLI) tool to create a prediction model for you. It also generates customizable sample code that uses the model.