experiment-tracking

Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.

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

SEML: Slurm Experiment Management Library

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

PyTorch Template for DL projects

Experiment tracking server focused on speed and scalability

Deploy MLflow with HTTP basic authentication using Docker

Parameterize Python scripts/notebooks all from the command line and run on cloud GPUs

Metadata store for Production ML

More light-weight pytorch experiment management library!

GitHub Action That Retrieves Model Runs From Weights & Biases

MLOps for deploying a Credit Risk model

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

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

Python’s delightfully modular experiment tracker for MLOps