EvalML builds, optimizes, and evaluates machine learning pipelines with automated data checks and objective functions.
Open source
24 alternativesLightwood
Lightwood builds and customizes machine learning pipelines using a declarative JSON-AI syntax.
About Lightwood
Lightwood is an open-source AutoML framework that generates machine learning pipelines from declarative JSON-AI syntax. It organizes work into preprocessing and cleaning, feature engineering with encoders, and model building and training with mixer models.
It works with pandas DataFrames and handles numbers, dates, categories, tags, text, arrays and multimedia formats, with a time-series mode for between-row dependencies. Users can keep defaults, change settings such as column type, or replace steps with custom methods, then generate Python code from the JSON-AI object and train a Predictor with predictor.learn().
Key features
- Generate machine learning pipelines using declarative JSON-AI syntax
- Detect column data types through statistical analysis
- Clean columns and create train/dev/test splits
- Convert data to model inputs with rule-based or learned encoders
- Use default mixers or custom models inheriting BaseMixer
- Support time-series mode for between-row dependencies
Best for: Python users working with pandas DataFrames who want to customize generated machine learning pipelines.
Good for
- Allows overriding defaults or replacing pipeline steps with custom methods
- Handles multiple data types together in one problem
- Generates Python code from the JSON-AI pipeline description
Watch out for
- Requires input data as pandas DataFrames
Lightwood at a glance
- Website
- github.com/mindsdb/lightwood
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Lightwood FAQ
How is a Lightwood pipeline structured?
Three steps: preprocessing and cleaning, feature engineering with encoders, and model building and training with mixer models.
What data can Lightwood process?
Numbers, dates, categories, tags, text, arrays and multimedia formats, with a time-series mode for between-row dependencies.
How do I customize a pipeline?
Edit JSON-AI values such as column type, or replace steps with custom encoders inheriting BaseEncoder or mixers inheriting BaseMixer.
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