Introducing ML.NET: Cross-platform, Proven and Open Source Machine Learning Framework
微软 正式开源 C#人工智能框架：https://github.com/dotnet/machinelearning
Today at //Build 2018, we are excited to announce the preview of ML.NET, a cross-platform, open source machine learning framework. ML.NET will allow .NET developers to develop their own models and infuse custom ML into their applications without prior expertise in developing or tuning machine learning models.
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 .
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.
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’s internal capabilities to ML.NET in open source.
To sum it all up, ML.NET is our commitment to make ML great in .NET.
The table below describes the entire list of components that are being released as a part of ML.NET 0.1.
We aim to make ML.NET’s APIs generic, such that other frameworks like CNTK, Accord.NET, TensorFlow and other libraries can become usable through one shared API.
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.
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.
The rest of this blog post provides more details about ML.NET; feel free to jump to the one that interests you the most.
- ML.NET Core Components
- Sentiment Classification with ML.NET
- The Road Ahead
- Help shape ML.NET for your needs
- 代号：dnc 2018
- 亮点：去中心化、社区驱动 开源峰会
- 时间：2018年5月20日 周日
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