GAMA review
A code-first, self-hosted AutoML package for tabular classification and regression.
Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
GAMA is a Python AutoML package for end users and AutoML researchers who want to search machine-learning pipelines for tabular classification or regression. It combines preprocessing, algorithms and tuned hyperparameters, and can bring tuned pipelines together as ensembles. Its scope suits small teams optimizing tabular ML pipelines, as well as solo or mid-market users comfortable working in code. It is free, open source and distributed under the Apache-2.0 license, rather than offered as a hosted commercial service.
The central capability is pipeline search under resource constraints: GAMA looks for optimized combinations of preprocessing and algorithms with tuned hyperparameters. Classification and regression are the stated problem types, and the project also provides prediction and probability prediction methods. Tuned pipelines can be combined into ensembles, while optimization progress logs support later analysis. This makes GAMA more focused on automating model selection and pipeline construction than on a broad visual workflow or automated feature engineering. Its workflow is code-based, and batch deployment is the listed deployment option.
GAMA is installed with pip and runs on Linux, macOS and Windows; hosting is self-managed. The project lists community support, so teams that need vendor-provided support or a hosted service should weigh that constraint. The README notes that its dashboard is temporarily disabled, which makes the code interface the practical route for now. Choose GAMA when open-source, self-hosted tabular pipeline search and ensemble building fit the team's workflow; consider another tool if visual workflow, automated feature engineering or a hosted environment is a priority.
GAMA pros and cons
- Where it wins
- Searches preprocessing, algorithms and tuned hyperparameters together
- Combines tuned pipelines into ensembles and logs optimization progress
- Provides prediction and probability prediction methods
- Where it doesn't
- Focused on tabular classification and regression
- Uses a code-based workflow rather than a dashboard
- Community support; the dashboard is temporarily disabled
GAMA fact sheet, pricing and score →
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