experiment-tracking

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

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

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

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!

An Agent Skill for the DL experiment lifecycle: RUN (a GPU you own or rent) → VERIFY the number is real → DELIVER reproducible, single-source figures and tables.

GitHub Action That Retrieves Model Runs From Weights & Biases

Build ML pipelines you can trust — visually on a canvas or in Python. Self-hosted platform + Apache-2.0 library. Catches data leakage, keeps scores honest, and takes you from raw data to monitored model.

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.