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.

Fast and Accurate ML in 3 Lines of Code

AutoML library for deep learning

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 🙏: MLOps for Reliable AI: from Classical ML to Agents. https://zenml.io.

cube studio开源云原生一站式机器学习/深度学习/大模型AI平台，mlops算法链路全流程，算力租赁平台，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虚拟化，边缘计算，标注平台自动化标注，deepseek等大模型sft微调/奖励模型/强化学习训练，vllm/ollama/mindie大模型多机推理，私有知识库，AI模型市场，支持国产cpu/gpu/npu 昇腾生态，支持RDMA，支持pytorch/tf/mxnet/deepspeed/paddle/colossalai/horovod/ray/volcano等分布式

Lightning ⚡️ fast forecasting with statistical and econometric models.

Merlion: A Machine Learning Framework for Time Series Intelligence

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

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

Fast and flexible AutoML with learning guarantees.

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

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