{"id":23900,"date":"2019-07-17T12:25:46","date_gmt":"2019-07-17T19:25:46","guid":{"rendered":"https:\/\/devblogs.microsoft.com\/dotnet\/?p=23900"},"modified":"2024-01-12T11:31:44","modified_gmt":"2024-01-12T19:31:44","slug":"announcing-ml-net-1-2-and-model-builder-updates-machine-learning-for-net","status":"publish","type":"post","link":"https:\/\/devblogs.microsoft.com\/dotnet\/announcing-ml-net-1-2-and-model-builder-updates-machine-learning-for-net\/","title":{"rendered":"Announcing ML.NET 1.2 and Model Builder updates (Machine Learning for .NET)"},"content":{"rendered":"

We are excited to announce ML.NET 1.2<\/a> and updates to Model Builder<\/a> and the CLI. ML.NET<\/a> is an open-source and cross-platform machine learning framework for .NET developers. ML.NET also includes Model Builder<\/a> (a simple UI tool for Visual Studio) and the ML.NET<\/a> CLI (Command-line interface) to make it super easy to build custom Machine Learning (ML) models using Automated Machine Learning (AutoML).<\/p>\n

Using ML.NET<\/a>, 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!<\/p>\n

The following are some of the key highlights in this update:<\/p>\n

ML.NET Updates<\/h2>\n

ML.NET<\/a> 1.2 is a backwards compatible release with no breaking changes so please update to get the latest changes.<\/p>\n

General availability of TimeSeries support for forecasting and anomaly detection<\/h3>\n

Developers can use the Microsoft.ML.TimeSeries package<\/a> for many scenarios such as: detecting spikes and changes in product sales using an anomaly detection model or creating sales forecasts which could be affected by seasonality and other time related context.<\/p>\n

Learn more through these samples<\/a>.<\/p>\n

<\/p>\n

General availability of ML.NET packages to use TensorFlow and ONNX models<\/h3>\n

ML.NET has been designed as an extensible platform so that you can consume other popular ML models such as TensorFlow and ONNX models and have access to even more machine learning and deep learning scenarios, like image classification, object detection, and more.<\/p>\n

Learn more through these code samples for Microsoft.ML.OnnxTransformer<\/a> and Microsoft.ML.TensorFlow<\/a> and the end-to-end ML.NET computer vision sample apps:<\/p>\n