ml-experiments

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

ML Experimentation Platform

lightweight MCP server for ClearML

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

AI Agent plugin for Autoresearch with AI (Claude, OpenClaw, etc) to improve anything!

Research-first machine learning experiment tracker for comparing model metrics, scalar curves, artifacts, and experiment lineage.

A hands-on collection of machine learning concepts, algorithms, and practical implementations. Designed to help learners and practitioners apply ML techniques with clarity and efficiency. 🚀

Paper-to-code replication workspace with Jupyter notebooks and Python implementations of ML and deep learning research ideas.

A self-improving ML agent that compounds capability across projects through knowledge crystallization, reusable skills and shared ML/MLOps infrastructure.

Nighttime ML experiment workflow with Claude Code multi-agent orchestration (Planner / Executor / Reviewer / Auditor)

A lightweight Python library for reproducible computational experiments with an ultra-simple, smart API. From idea to insight in under 5 minutes, with zero configuration.

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

A multi-agent research design council that uses Flask and Backboard AI to critique, refine, and evaluate experimental ideas.

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

MediaPipe face landmarker, detector, and recognition experiments.

Autonomous experiment loops for Claude Code. Let AI run 100 experiments while you sleep. Works on any codebase.

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

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.

Applied time-series forecasting and anomaly detection using ML and statistical baselines, with rigorous experimentation, residual-driven diagnostics, and reproducible evaluation workflows.

Automate text optimization with metric-driven loops for prompts, docs, and other files using OpenClaw