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🐶 A tool to package, serve, and deploy any ML model on any platform. Archived to be resurrected one day🤞

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

🏷️ Git Tag Ops. Turn your Git repository into Artifact Registry or Model Registry.

An Open-source, self-hosted AI model hub with Hugging Face compatibility, accelerating vLLM/SGLang performance.

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 simple app to help you find relevant machine learning papers to read.

An end to end ML project. Using MLflow for experiment tracking and model registry. Prefect for workflow orchestration. S3 for artifacts storage. AWS Lambda/ ECR for serverless model serving. AWS REST API gateway as endpoint to lambda function. GitHub Actions for CI/CD.

Hopsworks Machine Learning Api 🚀 Model management with a model registry and model serving

Production-grade MLOps: Model deployment, monitoring, feature stores, and ML pipelines for real-world AI systems.

🧙‍♂️ A professional machine learning GUI for scientists, developers & students — powered by PySide6 & scikit-learn. 📊 Load data, preprocess, train, evaluate, and predict — all without writing code. ⚡ Includes AutoML, hyperparameter tuning, model registry, experiment tracking, and a plugin system.

PolicyBind is an AI Policy-as-Code platform that enables organizations to govern AI usage through a unified model registry, real-time token-based access control, and automated compliance reporting for frameworks like the EU AI Act and NIST AI RMF.

Part of the Hydrosphere.io project.

Newron is a data-centric ML platform to easily build, manage, deploy and continuously improve models through data driven development.

Experiment tracking and model registry in the images segmentation project

An auto-updating AI Model Catalog available as a Golang Package, CLI Tool, or Server (RESTful, WebSockets, SSE).

Production MLOps pipeline for Paris bike traffic prediction. Airflow orchestration, MLflow tracking (Cloud SQL), FastAPI deployment. Features: automated ingestion, drift detection, champion/challenger models, Prometheus+Grafana monitoring, Discord alerts. 15 Docker services locally.

Automated MLOps for Urban Air Quality is a serverless AI system forecasting PM2.5 levels for Karachi, Pakistan. It automates the entire machine learning lifecycle from hourly data ingestion to daily model retraining, predicting air quality for 24, 48, and 72 hour horizons.

End-to-end ML lifecycle platform with reproducible training, policy-based promotion, progressive delivery, and rollback.