News

Microsoft Releases Open Source, Cross Platform Machine Learning Framework

• By David Ramel
• 05/08/2018

As part of its bevy of artificial intelligence (AI) announcements from Build 2018, on Monday Microsoft previewed ML.NET, a new cross-platform, open source machine learning framework.

The new framework was originally developed at Microsoft Research and was launched as part of the .NET Foundation.

The goal behind ML.NET is to help NET developers get in on cutting-edge ML programming without having to learn the underlying technical details associated with creating and tuning machine learning models. With ML.NET, devs can work with ML models in native .NET languages like C#, "without having to learn and use Python," as one developer said in comments section of the announcement post.

Designed to complement ML and AI development with the company's existing tools such as Azure Machine Learning and Cognitive Services, the early preview will in time receive more capabilities.

"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)," Microsoft said. "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."

Future supported scenarios, the company said, will include recommendation systems and anomaly detection. Deep learning functionality is also on tap, as it's planned to work with libraries such as TensorFlow, Caffe2 and CNTK, along with general machine learning libraries such as Accord.NET.

The v0.1 release, whose source code is available on GitHub, works on any platform supporting 64-bit .NET Core, including Windows, Linux and macOS. .NET Core 2.0 needs to be installed to work with the new framework. More information can be found in the full release notes and a developer guide.