Databricks AutoML

Developer tool

Automated machine learning that trains and evaluates classification, regression and forecasting models on a dataset, and writes an editable notebook with the training code for every trial.

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TypeDeveloper tool
RoleBundled component
AvailabilitySold
DeploymentSaaS
Intended forDeveloper, Business team
SecurityNot recorded
StatusActive
Sold withinDatabricks Data + AI Platform
PricingNot recorded

What it does

databricks.com

Sources

Pricing

Not found · 18 Oct 2026

Read the AutoML product page and documentation: neither states a price or a separate meter for AutoML.

Description

Official documentation · 18 Oct 2026

"Databricks AutoML allows you to quickly generate baseline models and notebooks"; "Leverage AutoML to tackle a variety of machine learning problems ranging from classification, regression, and forecasting."

What it does

Official documentation · 18 Oct 2026

"Automatically set up your machine learning project with the training libraries, MLflow integration for experiment tracking and built-in ML best practices such as training and testing split, normalizing of features and hyperparameter tuning."

Sold within

Not found · 18 Oct 2026

Documentation states compute requirements (Databricks Runtime for Machine Learning) but no sentence says which plan includes AutoML.

Name

Official documentation · 18 Oct 2026

"Databricks AutoML - Automated Machine Learning".

URL

Official documentation · 18 Oct 2026

The product's own page, listed in the Artificial Intelligence menu as "AutoML".

Deployment

Official documentation · 18 Oct 2026

AutoML runs on Databricks compute: "Use a compute resource with a supported compute access mode."

Interfaces

Official documentation · 18 Oct 2026

"Get started with AutoML experiments through a low-code UI for regression; classification; or forecasting, or the Python API."

Intended for

Official documentation · 18 Oct 2026

"ML experts can accelerate their workflow ... and citizen data scientists can quickly achieve usable results with a low-code approach."

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