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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Lightwood is Legos for Machine Learning.
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| 2026-07-31 | 508 |
| 2026-08-01 | 508 |
| 2026-08-06 | 508 |
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# Lightwood <!--- badges here? ---> Lightwood is an AutoML framework that enables you to generate and customize machine learning pipelines declarative syntax called JSON-AI. Our goal is to make the data science/machine learning (DS/ML) life cycle easier by allowing users to focus on **what** they want to do their data without needing to write repetitive boilerplate code around machine learning and data preparation. Instead, we enable you to focus on the parts of a model that are truly unique and custom. Lightwood works with a variety of data types such as numbers, dates, categories, tags, text, arrays and various multimedia formats. These data types can be combined together to solve complex problems. We also support a time-series mode for problems that have between-row dependencies. Our JSON-AI syntax allows users to change any and all parts of the models Lightwood automatically generates. The syntax outlines the specifics details in each step of the modeling pipeline. Users may override default values (for example, changing the type of a column) or alternatively, entirely replace steps with their own methods (ex: use a random forest model for a predictor). Lightwood creates a "JSON-AI" object from this syntax which can then be used to automatically generate python code to represent your pipeline. For details on how to generate JSON-AI syntax and how Lightwood works, check out the [Lightwood Philosophy](#Lightwood-Philosophy). ## Lightwood Philosophy Lightwood abstracts the ML pipeline into 3 core steps: (1) Pre-processing and data cleaning <br> (2) Feature engineering <br> (3) Model building and training <br> <p align="center"> <img src="/assets/lightwood.png" alt="Lightwood internals" width="800"/> </p> #### i) Pre-processing and cleaning For each column in your dataset, Lightwood will identify the suspected data type (numeric, categorical, etc.) via a brief statistical analysis. From this, it will generate a JSON-AI syntax. If the user keeps default behavior, Lightwood will perform a brief pre-processing approach to clean each column according to its identified data type. From there, it will split the data into train/dev/test splits. The `cleaner` and `splitter` objects respectively refer to the pre-processing and the data splitting functions. #### ii) Feature Engineering Data can be converted into features via "encoders". Encoders represent the rules for transforming pre-processed data into a numerical representations that a model can be used. Encoders can be **rule-based** or **learned**. A rule-based encoder transforms data per a specific set of instructions (ex: normalized numerical data) whereas a learned encoder produces a representation of the data after training (ex: a "\[CLS\]" token in a language model). Encoders are assigned to each column of data based on the data type; users can override this assignment either at the column-based level or at the data-type based level. Encoders inherit from the `BaseEncoder` class. #### iii) Model Building and Training We call a predictive model that intakes *encoded* feature data and outputs a prediction for the target of interest a `mixer` model. Users can either use Lightwood's default mixers or create their own approaches inherited from the `BaseMixer` class. We predominantly use PyTorch based approaches, but can support other models. ## Usage We invite you to check out our [documentation](https://mindsdb.github.io/lightwood/) for specific guidelines and tutorials! Please stay tuned for updates and changes. ### Quick use cases Lightwood works with `pandas.DataFrames`. Once a DataFrame is loaded, defined a "ProblemDefinition" via a dictionary. The only thing a user needs to specify is the name of the column to predict (via the key `target`). Create a JSON-AI syntax from the command `json_ai_from_problem`. Lightwood can then use this object to *automatically generate python code filling in the steps of the ML pipeline* via `code_from_json_ai`. You c
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:7aea65ab39a2d612, topic:pytorch