17 automated machine learning (AutoML) tools — every link checked and refreshed for 2026.
Auto-WEKA
Auto-WEKA simultaneously selects a learning algorithm and sets its hyperparameters using Bayesian optimization.
auto-sklearn
auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.
TPOT
TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
H2O AutoML
H2O AutoML automates algorithm selection, feature generation, hyperparameter tuning, iterative modeling, and model assessment.
TransmogrifAI
TransmogrifAI is an AutoML library written in Scala that runs on top of Apache Spark.
AutoKeras
AutoKeras is an AutoML system based on Keras. The goal of AutoKeras is to make machine learning accessible to everyone.
DataRobot Predictive AI (formerly AutoML)
DataRobot's AutoML capability now lives inside Predictive AI, empowering teams to build, tune, and deploy accurate models at scale without sacrificing time and trust.
Auto-PyTorch
Auto-PyTorch optimizes the network architecture and the training hyperparameters to enable fully automated deep learning (AutoDL).
B2Metric AutoML
B2Metric AutoML solution sets up an ML pipeline for usage of marketing teams, data scientists and data executives.
Google Cloud Vertex AI
Build, deploy, and scale ML models faster, with pre-trained and custom tooling within a unified AI platform.
PyCaret AutoML
This function returns the best model out of all models created in the current active environment based on the metric defined in the optimize parameter.
Auger.AI
Auger.AI is an AutoML platform. Auger's patented Bayesian optimization-based search of algorithm/hyperparameter combinations builds more accurate predictive models faster.
TAZI (Agentic AI for Financial Services)
TAZI has evolved from a general AutoML platform into grounded, growth-focused AI agents purpose-built for RIAs, community banks, and credit unions.
Azure AutoML
Azure AutoML allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality.