# dotnet/machinelearning

ML.NET is an open source and cross-platform machine learning framework for .NET.

Repository: https://github.com/dotnet/machinelearning
Canonical: https://ross.abutalabs.com/products/machinelearning
Homepage: https://dot.net/ml
Language: C#
License: MIT
License Family: permissive
Topics: machine-learning, algorithms, dotnet, ml
Last push: 2026-08-25T06:49:59+00:00

## Health v2 (maintenance only)
Score: 80/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 44, longevity 100
- inputs: {"age_days": 3044, "days_push": 8, "days_rel": 295, "gap_med": 42, "n_releases_24m": 4}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 9351, forks 1950 (observed 2026-08-28T04:10:30.781736+00:00)

## What it is
ML.NET is an open-source, cross-platform machine learning framework for .NET that lets developers build, train, and deploy custom ML models using C# or F#. It includes data loading and transformation pipelines, many ML algorithms, AutoML support, and can consume TensorFlow and ONNX models.

## Use cases
- train a classification model in C#
- add machine learning to a .NET application without Python
- detect anomalies in time series data
- run TensorFlow or ONNX models in .NET
- build a sentiment analysis model
- forecast values with .NET
- use AutoML to train models from .NET code

## When to choose
- you are a .NET developer wanting ML without learning Python
- you need to embed trained models directly in C#/F# applications
- you want cross-platform ML on Windows, Linux, and macOS including ARM64
- you need to consume TensorFlow or ONNX models within .NET

## When to avoid
- you need cutting-edge deep learning research tooling or large model training
- your team primarily works in Python with PyTorch or scikit-learn
- you need the broadest ecosystem of pretrained models and community libraries

## Facets
- artifact type: framework
- maturity: active
- function: machine-learning, data-science, etl
- domain: machine-learning, data-science, cross-platform
- platform: windows, dotnet, cross-platform
- tags: mlnet, automl, onnx, tensorflow, csharp, classification, anomaly-detection, forecasting, linux, macos

## Member repositories
- dotnet/machinelearning (main) score 80

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:30.781736+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:22:26.865925+00:00, confidence not recorded.
  - readme: https://github.com/dotnet/machinelearning (fetched 2026-08-28T04:10:30.781736+00:00, sha 2ca1c6e13682)
  - homepage: https://dot.net/ml (fetched 2026-08-29T08:22:26.672895+00:00, sha 4e0f91861335)
  - site_page: https://learn.microsoft.com/docs (fetched 2026-08-29T08:22:26.677898+00:00, sha f29800be2b9a)
  - site_page: https://learn.microsoft.com/en-us/dotnet (fetched 2026-08-29T08:22:26.675847+00:00, sha efa9016b58aa)
- Data as of 2026-08-30T08:39:29.467469+00:00.