{"id":17495,"date":"2018-05-07T17:41:28","date_gmt":"2018-05-08T00:41:28","guid":{"rendered":"https:\/\/blogs.msdn.microsoft.com\/dotnet\/?p=17495"},"modified":"2019-02-22T14:53:30","modified_gmt":"2019-02-22T21:53:30","slug":"introducing-ml-net-cross-platform-proven-and-open-source-machine-learning-framework","status":"publish","type":"post","link":"https:\/\/devblogs.microsoft.com\/dotnet\/introducing-ml-net-cross-platform-proven-and-open-source-machine-learning-framework\/","title":{"rendered":"Introducing ML.NET: Cross-platform, Proven and Open Source Machine Learning Framework"},"content":{"rendered":"

Today at \/\/Build 2018, we are excited to announce the preview of ML.NET, a cross-platform, open source machine learning framework<\/strong>. ML.NET will allow .NET developers\u00a0to develop their own models and infuse custom ML into their applications without prior expertise in developing or tuning machine learning models.<\/p>\n

ML.NET was originally developed in Microsoft Research and evolved into a significant framework over the last decade; it is used across many product groups in Microsoft like Windows, Bing, Azure, and more <\/strong>.<\/p>\n

With this first preview release, ML.NET enables ML tasks like classification (e.g. text categorization and sentiment analysis) and regression (e.g. forecasting and price prediction). Along with these ML capabilities, this first release of ML.NET also brings the first draft of .NET APIs for training models, using models for predictions, as well as the core components of this framework, such as learning algorithms, transforms, and core ML data structures.<\/p>\n

ML.NET is first and foremost a framework, which means that it can be extended to add popular ML Libraries like TensorFlow, Accord.NET, and CNTK. We are committed to bringing the full experience of ML.NET\u2019s internal capabilities to ML.NET in open source.<\/p>\n

To sum it all up, ML.NET is our commitment to make ML great in .NET.<\/p>\n

Please come and join us over on GitHub and help shape the future of ML in .NET:<\/p>\n

https:\/\/github.com\/dotnet\/machinelearning<\/a><\/p>\n

Over time, ML.NET will enable other ML scenarios like recommendation systems, anomaly detection, and other approaches, like deep learning, by leveraging popular deep learning libraries like TensorFlow, Caffe2, and CNTK, and general machine learning libraries like Accord.NET.<\/p>\n

ML.NET also complements the experience that Azure Machine Learning and Cognitive Services provides by allowing for a code-first approach, supports app-local deployment and the ability to build your own models.<\/p>\n

The rest of this blog post provides more details about ML.NET; feel free to jump to the one that interests you the most.<\/p>\n