Machine Learning

Introducing the ML.NET Text Classification API (preview)

June 14, 2022 Jun 14, 2022 06/14/22

Luis Quintanilla

Introducing the new ML.NET Text Classification API (preview) which uses state-of-the-art NLP transformer models and TorchSharp to train custom text classification models.

What’s new with ML.NET Automated ML (AutoML) and tooling

May 17, 2022 May 17, 2022 05/17/22

Luis Quintanilla

Check out what's new with ML.NET Automated ML (AutoML), tooling, and notebooks!

Training a ML.NET Model with Azure ML

February 28, 2022 Feb 28, 2022 02/28/22

Becca McHenry

Learn how to create a training pipeline for ML.NET using Azure ML and Azure Devops.

ML.NET Updates & Announcing Notebooks in Visual Studio

October 4, 2021 Oct 4, 2021 10/4/21

Bri Achtman

Check out what's new in ML.NET and Model Builder.

ML.NET June Updates

June 22, 2021 Jun 22, 2021 06/22/21

Bri Achtman

Check out what's new in ML.NET and Model Builder.

ML.NET Survey: Model Explainability

June 10, 2021 Jun 10, 2021 06/10/21

Jessie Houghton

Take the ML.NET Model Explainability survey to let us know what you want to see in ML.NET for explainability and fairness.

ML.NET Survey: Machine Learning in .NET

April 8, 2021 Apr 8, 2021 04/8/21

Bri Achtman

Take the ML.NET survey to let us know what you want to see next for Machine Learning in .NET.

ML.NET and Model Builder March Updates

March 15, 2021 Mar 15, 2021 03/15/21

Bri Achtman

This release of ML.NET and Model Builder brings numerous bug fixes and enhancements as well as new features, including config-based training and a redesigned Consume step.

ML.NET Model Builder November Updates

November 30, 2020 Nov 30, 2020 11/30/20

Bri Achtman

This release of ML.NET Model Builder brings numerous bug fixes and enhancements as well as new features, including advanced data loading options and streaming training data from SQL.

MLOps: DevOps for Machine Learning

October 13, 2020 Oct 13, 2020 10/13/20

Bri Achtman

Machine Learning Operations (MLOps) is like DevOps for the machine learning lifecycle. This includes things like model deployment & management and data tracking, which help with productionizing machine learning models. Through the survey below, we'd love to get feedback on your current DevOps practices as well as your prospective usage ...

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