Discover top open-source software, updated regularly with real-world adoption signals.
Hands-free AutoML that plugs directly into scikit-learn
auto-sklearn provides automated model selection and hyperparameter optimization as a drop-in scikit-learn estimator, leveraging Bayesian optimization and meta-learning for fast, robust pipelines.
auto-sklearn is a Python toolkit that turns the tedious process of model selection, hyperparameter tuning, and ensemble construction into a single, scikit-learn‑compatible estimator. By simply importing or , users can fit powerful models with just a few lines of code.
AutoSklearnClassifierAutoSklearnRegressorThe library combines Bayesian optimization (via SMAC) with meta‑learning from prior tasks to warm‑start searches, dramatically reducing the time needed to find high‑performing configurations. It automatically builds ensembles of the best models, improving robustness and generalization. All of this works within the familiar fit/predict API, making it easy to integrate into existing pipelines, notebooks, or production code.
auto-sklearn runs on standard Python environments and requires only a CPU (GPU optional for underlying estimators). It is released under a BSD‑3‑Clause license, with extensive documentation, examples, and a growing community on GitHub. Suitable for research, rapid prototyping, and production‑grade AutoML workflows.
When teams consider auto-sklearn, these hosted platforms usually appear on the same shortlist.
Cloud service for accelerating and managing the machine learning project lifecycle, including training and deployment of models
Automated machine learning platform for building AI models without coding
Unified ML platform for training, tuning, and deploying models
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Rapid prototyping of classification models
Generate a performant classifier in minutes without manual hyperparameter tuning
Automated preprocessing for tabular data
Leverage built‑in feature engineering pipelines to produce ready‑to‑use models
Benchmarking multiple algorithms across datasets
Obtain comparative performance reports automatically
Deploying robust ensembles in production
Create ensembles that improve generalization and reduce variance
It automates model selection, hyperparameter optimization, and ensemble building while exposing the same fit/predict interface.
Classification and regression on tabular data using any scikit-learn estimator.
No, it runs on CPU; GPU can accelerate underlying models if they support it.
It uses prior runs on similar datasets to suggest promising configurations, reducing search time.
Yes, with regular releases, documentation, and a BSD‑3‑Clause license on GitHub.
Project at a glance
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