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 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 minimal Python logger that tracks everything you try when building AI - metrics, prompts, models, etc, so you can see what changed and why.
A curated list of awesome open source and commercial MLOps platforms 🚀