Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.

MLRun is an open source MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications.

Visualise your Kedro data and machine-learning pipelines and track your experiments.

A simple & elegant experiment tracking framework that integrates persistence logic & best practices directly into Python

Code for Kaggle and Offline Competitions

A Clojure machine learning library

PyTorch template for Deep Learning projects with support for scalable multi-GPU and multi-node training.

SEML: Slurm Experiment Management Library

A curated list of awesome open source tools and commercial products for ML Experiment Tracking and Management 🚀

RapidFire AI: Rapid AI Customization from RAG to Fine-Tuning

Experiment tracking server focused on speed and scalability

Deploy MLflow with HTTP basic authentication using Docker

Metadata store for Production ML

More light-weight pytorch experiment management library!

GitHub Action That Retrieves Model Runs From Weights & Biases

Build and ship production ML pipelines faster: a pipeline library with an optional self-hosted visual layer for modular, reproducible workflows, local testing, and experiment tracking.

MLOps for deploying a Credit Risk model

A curated list of awesome open source and commercial MLOps platforms 🚀

A minimal Python logger that tracks everything you try when building AI - metrics, prompts, models, etc, so you can see what changed and why.

Ralph Loop Optimizer: an AI-driven framework that turns any evaluatable codebase into a self-improving optimization loop for strategies, models, prompts, and workflows