Docs for Chalk AI

This is a collection of all the machine learning techniques required in any machine learning project. It contains detailed descriptions, videos, book recommendations, and additional material to properly grasp all the concepts.

Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage and metadata. Runs and scales everywhere python does.

Alink is the Machine Learning algorithm platform based on Flink, developed by the PAI team of Alibaba computing platform.

🔬motif-learn: machine learning in scanning transmission electron microscopy

The DBT of ML, as Aligned describes data dependencies in ML systems, and reduce technical data debt

An open source python library for automated feature engineering

Data Science Feature Engineering and Selection Tutorials

GitHub Action that wraps the Chalk CLI

EvalML is an AutoML library written in python.

Go client for Chalk

All Relevant Feature Selection

Code supplement for "Neuroevolutionary representations for learning heterogeneous treatment effects"

Low code machine learning library, specified for insurance tasks: prepare data, build model, implement into production.

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

Time-series machine learning at scale. Built with Polars for embarrassingly parallel feature extraction and forecasts on panel data.