automl

🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

AutoML library for deep learning

Fast and Accurate ML in 3 Lines of Code

Automated Machine Learning with scikit-learn

An open source python library for automated feature engineering

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

Google Brain AutoML

ZenML 🙏: The bridge between ML and Ops. https://zenml.io.

Merlion: A Machine Learning Framework for Time Series Intelligence

Lightning ⚡️ fast forecasting with statistical and econometric models.

cube studio开源云原生一站式机器学习/深度学习/大模型AI平台，支持sso登录，大数据平台对接，notebook在线开发，拖拉拽任务流pipeline编排，多机多卡分布式训练，超参搜索，推理服务VGPU，边缘计算，标注平台，自动化标注，大模型微调，vllm大模型推理，llmops，私有知识库，AI模型应用商店，支持模型一键开发/推理/微调，支持国产cpu/gpu/npu芯片，支持RDMA，支持pytorch/tf/mxnet/deepspeed/paddle/colossalai/horovod/spark/ray/volcano分布式，deepseek训练推理

A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.

A curated list of automated machine learning papers, articles, tutorials, slides and projects

Differentiable architecture search for convolutional and recurrent networks

AutoRAG: An Open-Source Framework for Retrieval-Augmented Generation (RAG) Evaluation & Optimization with AutoML-Style Automation

Fast and flexible AutoML with learning guarantees.

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning