EvalML

Overview

EvalML is an open-source AutoML library for Python. It automates the process of building, tuning, and evaluating machine learning models. The library provides a unified interface for common tasks like data splitting, feature engineering, and model selection.

Best for

Best for
Data scientists who want to rapidly prototype and compare models without writing extensive code.

Use cases

• Automating model selection for classification and regression tasks
• Quickly prototyping machine learning pipelines
• Comparing multiple algorithms with minimal code

Notes

EvalML is an open-source AutoML library for Python. It automates the process of building, tuning, and evaluating machine learning models. The library provides a unified interface for common tasks like data splitting, feature engineering, and model selection.

849 stars on GitHub. Last updated 2026-01-14. Licensed BSD-3-Clause.

Use cases

• Automating model selection for classification and regression tasks
• Quickly prototyping machine learning pipelines
• Comparing multiple algorithms with minimal code

Pros

• Simplifies the machine learning workflow with a high-level API
• Supports a variety of algorithms and preprocessing steps
• Open-source with community contributions

Cons

• Limited to supervised learning tasks
• May not handle very large datasets efficiently
• Less flexible than manual tuning for complex problems

Indexed from awesome-llmops and enriched against its public facts.