Topics / #automl

Automl projects on GitHub

Automated machine learning that searches models and hyperparameters for you.

As of , the most-starred Automl projects on GitHub are best-of-ml-python (23.8K stars), awesome-production-machine-learning (21K stars) and nni (14.4K stars). GitHub.Locker tracks 6 Automl repositories with 80.3K stars combined. The fastest-growing on October 16 was awesome-production-machine-learning, with +3 stars in a day.

6Projects
80.3KStars in total
+5Stars in a day

Hot right now

Most stars gained in a day · Oct 16

The all-time top

6 projects · by stars

  1. #1 · lukasmasuch/best-of-ml-python

    best-of-ml-python

    A curated, ranked index of top open-source machine learning libraries for Python, covering areas like deep learning, NLP, AutoML and data visualization, generated and refreshed automatically.

    23.8K
  2. #2 · EthicalML/awesome-production-machine-learning

    awesome-production-machine-learning

    A curated, community-maintained directory of open source libraries for production machine learning: tools to deploy, monitor, version, scale, and secure ML systems, grouped into 25+ annotated sections.

    21K
  3. #3 · microsoft/nni

    nni

    NNI (Neural Network Intelligence) is Microsoft's open-source AutoML toolkit that automates the machine learning lifecycle: feature engineering, neural architecture search, model compression, and hyperparameter tuning.

    14.4K
  1. 4

    autogluon

    10.8Kautogluon/autogluon · Python

    AutoGluon is an open-source Python library that automates machine learning: feed it your data and it delivers strong predictive performance in about three lines of code.

  2. 5

    tpot

    10.1KEpistasisLab/tpot · Jupyter Notebook

    TPOT, the Tree-based Pipeline Optimization Tool, is a Python AutoML library that uses genetic programming to automatically optimize machine learning pipelines for your data; the README calls it your Data Science Assistant.

  3. 6

    AutoLOGIC

    209sethGu/AutoLOGIC · Python

    AutoLOGIC is an agentic framework for automated tabular machine learning across classification, regression, and clustering. It turns a task specification into coordinated feature, model, optimization, and output-control plans and then executes them with task-specific tools.

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