Welcome to the world of Machine Learning with ML.NET 1.0 - BRK3011 by Microsoft Developer Machine Learning with ML.NET 1.0 from Build 2019 by Microsoft Developer Create a Recommendation Model in ML.NET ML.NET End-to-End: Wine Regression with Database Data A Look at the ML.NET Model Builder Zeeshan Siddiqui - ML.NET by Machine Learning at Microsoft Build a ML.NET Machine Learning Model in F# A Quick Tour of the ML.NET CLI ML.NET End-to-End 2: Building a Web API Cross Validation in ML.NET Auto Train Machine Learning Models with ML.NET and AutoML Clustering in ML.NET Anomaly Detection Transform in ML.NET How to Read and Write an ONNX Model in ML.NET August 6, 2019 - Trying something new: ML.NET and recognizing Jeff's hats! Welcome to the world of Machine Learning with ML.NET 1.0 - BRK3011 by Microsoft Developer Machine Learning with ML.NET 1.0 from Build 2019 by Microsoft Developer Create a Recommendation Model in ML.NET ML.NET End-to-End: Wine Regression with Database Data A Look at the ML.NET Model Builder Zeeshan Siddiqui - ML.NET by Machine Learning at Microsoft Build a ML.NET Machine Learning Model in F# A Quick Tour of the ML.NET CLI ML.NET End-to-End 2: Building a Web API Cross Validation in ML.NET Auto Train Machine Learning Models with ML.NET and AutoML Clustering in ML.NET Anomaly Detection Transform in ML.NET How to Read and Write an ONNX Model in ML.NET August 6, 2019 - Trying something new: ML.NET and recognizing Jeff's hats!

(No summary)

We are excited to announce ML.NET 1.2 and updates to Model Builder and the CLI. ML.NET is an open-source and cross-platform machine learning framework for .NET developers. ML.NET also includes Model Builder (a simple UI tool for Visual Studio) and the ML.NET CLI (Command-line interface) to make it super easy to build custom Machine Learning (ML) models using Automated Machine Learning (AutoML). Using ML.NET, developers can leverage their existing tools and skill-sets to develop and infuse custom ML into their applications by creating custom machine learning models for common scenarios like Sentiment Analysis, Price Prediction, Image Classification and more! ML.NET 1.2 is a backwards compatible release with no breaking changes so please update to get the latest changes.

Today, coinciding with //BUILD 2019/ conference, we're thrilled by launching ML.NET 1.0 release! You can read the official ML.NET 1.0 release announcement Blog Post here and get started at the ML.NET site here. In this blog post I'm providing quite a few additional technical details along with my personal vision that you might find interesting, though. This is the first main milestone of a great journey in the open that started on May 2018 when we released ML.NET 0.1 as open source. Since then we've been releasing monthly, 12 preview releases plus this final 1.0 release, as shown in the roadmap below: The diagram above shows the the development in the open of ML.NET, however, as explained below, ML.NET has been internally used by Microsoft for quite a few years and used by other Microsoft products such as Bing Ads, Office, Windows, Azure, etc. ML.NET is an open-source and cross-platform machine learning framework (Windows, Linux, macOS), created by Microsoft, for .NET developers.

We are excited to announce the release of ML.NET 1.0 today. ML.NET is a free, cross-platform and open source machine learning framework designed to bring the power of machine learning (ML) into .NET applications. ML.NET allows you to train, build and ship custom machine learning models using C# or F# for scenarios such as sentiment analysis, issue classification, forecasting, recommendations and more. You can check out these common scenarios and tasks at our ML.NET samples repo. ML.NET was originally developed within Microsoft Research, and evolved into a significant framework used by many Microsoft products such as Windows Defender, Microsoft Office (Powerpoint design ideas, Excel Chart recommendations), Azure Machine Learning, PowerBI key influencers to name a few!