Best AutoML Software in 2026

AutoKeras suits people building machine-learning models through code who want automated model selection and configuration search.

FEDOT suits developers and researchers who want to automate or guide machine-learning pipeline creation in code.

It suits developers and machine-learning practitioners working in Linux, self-hosted Python environments.

AutoGluon

It suits Python users building predictive models from tables, time series, images, text, or other listed data types.

JADBio suits life scientists, research institutions, biotech and pharma companies, and other scientists working with study or public data.

MLJAR AutoML suits users building supervised models for tabular classification or regression who want configurable training modes and experiment reports.

Obviously AI

Obviously AI suits individuals, hobbyists, non-profits, and teams wanting no-code predictive modeling in a browser.

DataRobot

DataRobot is aimed at global enterprises, including regulated organizations in manufacturing, public sector, life sciences, insurance, financial services, and other listed sectors.

H2O describes data scientists, DevOps and IT professionals, and business analysts as audiences for Driverless AI.

FLAML suits developers and data scientists building AutoML, forecasting, ranking, or model-tuning workflows.

LightAutoML suits data scientists and analysts working with tabular or text data who want automated pipeline creation.

Vertex AI Vector Search

It suits teams building semantic or hybrid search, recommendation, retrieval-augmented generation, chatbot, and multimodal applications on Google Cloud.