production-machine-learning

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models

Code For The Issue Label Bot, an App that automatically labels issues using machine learning, available on the GitHub Marketplace. This is also code for the blog article: "How to automate tasks on GitHub with machine learning for fun and profit"

⚓ Eurybia monitors model drift over time and securizes model deployment with data validation

Free Open-source ML observability course for data scientists and ML engineers. Learn how to monitor and debug your ML models in production.

A modern, enterprise-ready business intelligence web application

Python library for Modzy Machine Learning Operations (MLOps) Platform

The official JavaScript SDK for the Modzy Machine Learning Operations (MLOps) Platform.

The official Java library for the Modzy Machine Learning Operations (MLOps) Platform

The Golang library for Modzy Machine Learning Operations (MLOps) Platform

Production-grade ML platform for biotech research intelligence. Multi-agent RAG system with LangGraph for discovering papers, companies, and funding. Features semantic search, knowledge graphs, and full MLOps stack. Built to showcase end-to-end ML/AI engineering skills.

Sharing my journey to be fluent in writing machine learning models for interesting datasets from competitions from kaggle , AICrowd and other ensemble of deep learning model techniques and then trying to get them to production.

Fraud Risk Modeling for Credit Card Transactions: an end-to-end Machine Learning (ML) project utilizing advanced algorithms to detect fraudulent activities and mitigate Fraud Risk.

🚀 Implement drifting models in PyTorch for one-step generation, learning to generate samples that resemble real data without iterative sampling.

End to end machine learning models deployment to production for a bank marketing problem.

Self-study course on designing production ML systems: 18 modules from data quality and feature pipelines to serving, monitoring and drift, plus lead-level interview quizzes and a runnable end-to-end churn project (PyTorch, Optuna, ONNX, FastAPI).

ProductionML.{org,com} website