DataBricks AutoML

About

Databricks AutoML allows users to quickly generate baseline models and notebooks. The vendor states it helps ML experts accelerate their workflow by fast-forwarding through trial-and-error to focus on customizations, while citizen data scientists can achieve results with a low-code approach. The product provides training code for every trial run to help data scientists jump-start development and assess the feasibility of using a dataset for machine learning. It tackles classification, regression, and forecasting problems using multiple algorithms from libraries including Scikit Learn, Apache Spark, XGBoost, Prophet, SHAP, and LightGBM. The page describes a "glass box" approach that provides generated editable notebooks for customizing baseline models and explaining how models were trained to fulfill audit and compliance requirements. The tool automatically sets up machine learning projects with training libraries, MLflow integration for experiment tracking, and built-in ML best practices such as training and testing split, normalizing features, and hyperparameter tuning.

Read from databricks.com on 2026-08-26. We describe what the vendor states; we do not audit it.

Composite of public compliance, deployment, integration, adoption, and company signals. “Not disclosed” reflects gaps in available data, not a vendor deficiency.

Enterprise Use Cases

Integrations

How DataBricks AutoML compares

Peers from the same category, ranked by Enterprise Readiness. Readiness is a composite of cataloged compliance, deployment, integration, adoption, and company signals.

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