{"id":25195,"date":"2019-11-06T10:01:16","date_gmt":"2019-11-06T17:01:16","guid":{"rendered":"https:\/\/devblogs.microsoft.com\/dotnet\/?p=25195"},"modified":"2019-11-13T18:06:56","modified_gmt":"2019-11-14T01:06:56","slug":"announcing-ml-net-1-4-global-availability-machine-learning-for-net","status":"publish","type":"post","link":"https:\/\/devblogs.microsoft.com\/dotnet\/announcing-ml-net-1-4-global-availability-machine-learning-for-net\/","title":{"rendered":"Announcing ML.NET 1.4 general availability (Machine Learning for .NET)"},"content":{"rendered":"

Coinciding with the Microsoft Ignite 2019<\/strong> conference, we are thrilled to announce the GA release of ML.NET<\/a> 1.4<\/strong> and updates to Model Builder<\/strong> in Visual Studio, with exciting new machine learning features that will allow you to innovate your .NET applications.<\/p>\n

ML.NET<\/a> is an open-source and cross-platform machine learning framework for .NET developers. ML.NET<\/a> also includes Model Builder (easy to use UI tool in Visual Studio) and 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 skillsets to develop and infuse custom ML into their applications by creating custom machine learning models for common scenarios like Sentiment Analysis, Price Prediction, Sales Forecast prediction, Customer segmentation, Image Classification<\/em> and more!<\/p>\n

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

ML.NET Updates<\/h2>\n

In ML.NET<\/a> 1.4 GA we have released many exciting improvements and new features that are described in the following sections.<\/p>\n

Image classification based on deep neural network retraining with GPU support (GA release)<\/h2>\n

\"ML.NET,<\/p>\n

This feature enables native DNN (Deep Neural Network) transfer learning with ML.NET targeting image classification.<\/p>\n

For instance, with this feature you can create your own custom image classifier model by natively training a TensorFlow model from ML.NET<\/a> API with your own images.<\/p>\n

Image classifier scenario \u2013 Train your own custom deep learning model with ML.NET<\/em><\/p>\n

\"Image<\/p>\n

ML.NET<\/a> uses TensorFlow<\/a> through the low-level bindings provided by the Tensorflow.NET library<\/a>. The advantage provided by ML.NET<\/a> is that you use a high level API very simple to use so with just a couple of lines of C# code you define and train an image classification model. A comparable action when using the low level Tensorflow.NET library<\/a> would need hundreds of lines of code.<\/p>\n

The Tensorflow.NET library<\/a> is an open source and low-level API library that provides the .NET Standard bindings for TensorFlow. That library is part of the open source SciSharp stack libraries<\/a>.<\/p>\n

The below stack diagram shows how ML.NET<\/a> is implementing these new features on DNN training.<\/p>\n

\"DNN<\/p>\n

As the first main scenario for high level APIs, we are currently providing image classification<\/strong>, but the goal in the future for this new API is to allow easy to use DNN training for additional scenarios such as object detection<\/strong> and other DNN scenarios in addition to image classification, by providing a powerful yet simple API very easy to use.<\/p>\n

This Image-Classification feature was initially released in v1.4-preview. Now, we\u2019re releasing it as a GA release<\/strong> plus we\u2019ve added the following new capabilities<\/strong> for this GA release:<\/p>\n

Improvements in v1.4 GA for Image Classification<\/h3>\n

The main new capabilities in this feature added since v1.4-preview are:<\/p>\n