News

Microsoft Offers Engineer to Help You Take ML.NET into Production

Microsoft's latest update of its ML.NET open source machine learning framework comes with a twist: The company is offering to provide an engineer for one-on-one help to get it working in production use.

Announced last May at the 2018 Build developer conference, ML.NET helps developers apply their .NET and C# or F# skills to integrate custom machine learning into an application. It aims to simplify the process of developing or tuning machine learning models for everyone, even if they don't have prior expertise in such tuning.

These ML models can be used for projects needing functionality such as sentiment analysis, recommendation, image classification and so on.

ML.NET 0.11 was described as a stability-focused release, and even though it appears to be a long way from general availability, Microsoft is offering to help get it set up for production use today.

The company said in a post last week: "If you are using ML.NET in your app and looking to go into production, you can talk to an engineer on the ML.NET team to:

  • Get help implementing ML.NET successfully in your application.
  • Provide feedback about ML.NET.
  • Demo your app and potentially have it featured on the ML.NET homepage, .NET Blog, or other Microsoft channel.

Microsoft provided a form to fill out for that feedback mechanism, which asks a series of questions and lets developers leave their contact information if they want someone from the ML.NET team to contact them.

In the meantime, the team said all future releases leading up to version 1.0 will focus on stability, with API refinements, bug fixes, reduction of the public API surface, improved documentation and samples, and more.

Two notable highlights of the 0.11 update were listed as: new capabilities to support text input when working with the popular TensorFlow, which facilitates working with text analysis scenarios such as sentiment analysis; some renaming of ONNX-related terms "to make the distinction between ONNX conversion and transformation clearer." ONNX is an open and interoperable model format that lets developers take models trained in one ML framework and use them in another framework.

About the Author

David Ramel is an editor and writer for Converge360.

comments powered by Disqus

Featured

  • Multi-Class Classification Using PyTorch: Model Accuracy

    Dr. James McCaffrey of Microsoft Research continues his four-part series on multi-class classification, designed to predict a value that can be one of three or more possible discrete values, by explaining model accuracy.

  • Python in VS Code Adds Data Viewer for Debugging

    The January 2021 update to the Python Extension for Visual Studio Code is out with a short list of new features headed by a data viewer used while debugging.

  • GitHub Ships Enterprise Server 3.0 Release Candidate

    It's described as "the biggest ever change to Enterprise Server," with improvements to Actions, Packages, mobile, security and more.

  • Attacks on .NET Apps Grow in Number, Severity, Says Security Firm

    .NET apps were found to have more serious vulnerabilities and suffer more attacks last year, according to data gathered by Contrast Labs.

Upcoming Events