2 Lines of code to track ML experiments + EDA + check into Github

ML Experimentation Platform

Unsorted Playground for Machine Learning, Reinforcement Learning and other AI Experiments

Contains code and slides for our talk at Cloud Next 2022: Better Hardware Provisioning for ML Experiments on GCP.

An application of the WhizML codebase for an analysis of cardiovascular disease risk.

A reusable codebase for fast data science and machine learning experimentation, integrating various open-source tools to support automatic EDA, ML models experimentation and tracking, model inference, model explainability, bias, and data drift analysis.

An application of the WhizML codebase for an analysis of Walmart weekly sales.

Docker for getting jax to work with cuda, for reproducing ml experiments like eicl. Sure, let's NOT make a compatibility matrix and let people fight for their lives on cuda