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The ML.NET Roadmap

The goal of ML.NET project is to provide an easy to use, .NET-friendly ML platform. This document describes the tentative plan for the project in the short and long-term.

ML.NET is a community effort and we welcome community feedback on our plans. The best way to give feedback is to open an issue in this repo. It's always a good idea to have a discussion before embarking on a large code change to make sure there is not duplicated effort. Many of the features listed on the roadmap already exist in the internal version of the code-base. They are marked with (*). We plan to release more and more internal features to Github over time.

In the meanwhile, we are looking for contributions. An easy place to start is to look at up-for-grabs issues on Github

Short Term

Training Improvements

  • Deep Learning Training Support
    • Integrate with leading DNN package(s)
    • Support for transfer learning.
    • Hybrid training of pipelines containing both DNN and non-DNN predictors.
    • Fast.ai like APIs.

Trained Model Management

  • Export models to ONNX (*)

Longer Term

Training Improvements

  • Add more learners, perhaps, including: (*)
  • Integration with other ML packages
    • Accord.NET
    • etc.
  • Additional ML tasks (*)
    • Sequence Classification - learns from a series of examples in a sequence, and each item is assigned a distinct label, akin to a multiclass classification task
  • Additional Data source support
    • Data from SQL Databases, such as SQL Server
    • Data located on the cloud
    • Apache Parquet
    • Native Binary high-performance format
  • Distributed Training
    • Easily train models on the cloud
  • Whole-pipeline optimizations for both training and inference
  • Automation of more data science tasks
  • Additional Trainers
  • Additional tasks

Featurization Improvements

  • Improved data wrangling support
  • Add auto-suggestion of training pipelines. The technology will provide intelligent LearningPipeline suggestions based on training data attributes (*)
  • Additional natural language text preprocessing
  • Time series and forecasting
  • Support for Video, audio, and other data types

Trained Model Management

  • Model operationalization in the Cloud
  • Model deployment on mobile platforms
  • Ability to run ONNX models in the LearningPipeline
  • Support for the next version of ONNX
  • Model deployment to IOT devices

GUI Improvements

  • Usability improvements
  • Support of additional ML.NET features
  • Improved code generation for training and inference
  • Run the pipelines rather than just suggesting them; present to the user the pipelines and the metrics generated from running.
  • Distributed runs, rather than sequential.

Other

  • Support for additional languages
  • Published reproducible benchmarks against industry-leading ML toolkits on a variety of tasks and datasets