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The ML.NET Roadmap

The goal of the ML.NET project is to make .NET developers great at machine learning. This document describes the plan for the project.

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.

We also invite contributions. The up-for-grabs issues on GitHub are a good place to start.

Goals through June 30, 2020

Test stability

Continuous integration builds currently have a 30% pass rate. We aim to get this pass rate up to at least 80%.

Streaming metrics

Currently, the way ML.NET computes metrics is memory-intensive. We will compute metrics in a streaming fashion instead, thereby reducing memory consumption.

Multivariate anomaly detection

ML.NET already supports univariate anomaly detection, but we will add the ability to detect anomalies in multiple variables over time.

ONNX Runtime exportability

We will expand the number of ML.NET transforms and estimators that are exportable to the ONNX Runtime.