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ML.NET 0.4 Release Notes

docs/release-notes/0.4/release-0.4.md

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ML.NET 0.4 Release Notes

Today we are releasing ML.NET 0.4. During this release we have started exploring new APIs for ML.NET that enable functionality that is missing from the current APIs. We welcome feedback and contributions to the conversation (relevant issues can be found here). While the focus has been on designing the new APIs, we have also moved several components from the internal codebase to ML.NET.

Installation

ML.NET supports Windows, MacOS, and Linux. See supported OS versions of .NET Core 2.0 for more details.

You can install ML.NET NuGet from the CLI using:

dotnet add package Microsoft.ML

From package manager:

Install-Package Microsoft.ML

Release Notes

Below are some of the highlights from this release.

  • Added SymSGD learner for binary classification (#624)

    • SymSGD is a technique for parallelizing SGD (Stochastic Gradient Descent). This enables it to sometimes perform faster than existing SGD implementations (e.g. Hogwild SGD).
    • SymSGD is available for binary classification, but can be used in multiclass classification with One-Versus-All
    • SymSGD requires adding the Microsoft.ML.HalLearners NuGet package to your project
    • The current implementation in ML.NET does not yet have multi-threading enabled due to build system limitations (tracked by #655), but SymSGD can still be helpful in scenarios where you want to try many different learners and limit each of them to a single thread.
    • Documentation can be found here

  • Added Word Embeddings Transform for text scenarios (#545)

    • Word embeddings is a technique for mapping words or phrases to numeric vectors of relatively low dimension (in comparison with the high dimensional n-gram extraction). These numeric vectors are intended to capture some of the meaning of the words so they can be used for training a better model. As an example, SSWE (Sentiment-Specific Word Embedding) can be useful for sentiment related tasks.
    • This transform enables using pretrained models to get the embeddings (i.e. the embeddings are already trained and available for use).
    • Several options for pretrained embeddings are available: GloVe, fastText, and SSWE. The pretrained model is downloaded automatically on first use.
    • Documentation can be found here.

  • Improved support for F# by allowing use of property-based row classes (#616)

    • ML.NET now supports F# record types.
    • The ML.NET samples repository is being updated to include F# samples as part of #36.

Additional issues closed in this milestone can be found here.

Acknowledgements

Shoutout to dsyme, SolyarA, dan-drews, bojanmisic, jwood803, sharwell, JoshuaLight, and the ML.NET team for their contributions as part of this release!