Pycaret

PyCaret is an open-source, low-code machine learning library for Python that automates model training, comparison, and deployment workflows. Founded in 2019 by Moez Ali, the project originated as a personal side project to reduce repetitive coding tasks in data science. It has since grown into a community-maintained project hosted on GitHub, with 9,800 stars and 1,900 forks as of May 2026.

PyCaret does not operate as a venture-backed company; it has no disclosed funding rounds, no paid employees, and no formal revenue. The project is maintained by volunteers and led by Moez Ali, who also works as a data scientist at a separate organization. PyCaret's product is a single Python library that integrates with scikit-learn, XGBoost, LightGBM, and other popular ML frameworks.

It offers modules for classification, regression, clustering, anomaly detection, natural language processing, and time series forecasting. The library is designed for rapid prototyping: a user can train and compare dozens of models with a few lines of code. PyCaret is used by individual data scientists, academic researchers, and small teams in enterprises that lack dedicated machine learning infrastructure.

Notable adopters include Microsoft, which has featured PyCaret in its Azure Machine Learning documentation, and the University of California, Berkeley, which uses it in coursework. In April 2026, the project relicensed its engine from MIT to the Functional Source License 1.1, MIT Future variant, a move that restricts commercial competitors from using PyCaret as the basis of a competing AutoML product while allowing non-competing internal use. The same month, the maintainers introduced a Claude Code-first contributor workflow, enabling community members to use Anthropic's AI agent to fix issues and submit pull requests.

PyCaret faces competition from commercial AutoML platforms such as H2O.ai, DataRobot, and Google's Vertex AI, as well as from other open-source libraries like AutoGluon and FLAML. The project has no dedicated funding for infrastructure or development, and its long-term sustainability depends on continued volunteer contributions and community goodwill.