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

A comprehensive guide designed to empower readers with advanced strategies and practical insights for developing, optimizing, and deploying scalable AI models in real-world applications.

Репозиторий направления Production ML, весна 2021

Lead Scoring: Optimizing SaaS Marketing-Sales Funnel by Extracting the Best Leads with Applied Machine Learning

Real-time fraud detection system using ensemble ML models, featuring streaming data processing, explainable AI with SHAP, and production-ready deployment with FastAPI and Docker.

This project is made to help you scale from a basic Machine Learning project for research purposes to a production grade Machine Learning web service

Complete AI/ML curriculum: From Python basics to production systems. 800+ notebooks covering transformers, embeddings, RAG, vector DBs, MLOps, NLP, computer vision & more.

🛰️ Production-ready ML system for geomagnetic storm prediction | 98% AUC, 70% recall | Threshold-optimized ensemble with real-time inference | 29-year dataset (1996-2025) | NOAA SWPC operational standards | Complete MLOps pipeline

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

Comprehensive scikit-learn ML handbook with 24 runnable Jupyter notebooks using built-in datasets. Covers regression, classification, ensembles, clustering, dimensionality reduction, and production pipelines - from beginner to senior level.

The objective of this coding exercice is to train a simple neural network on the mnist dataset in order to classify the handwritten digits into numbers ranging from zero to 9.

Production-ready ML pipeline for regression tasks with modular architecture (0.94 R², Kaggle validated)

An Enterprise AI Document Intelligence Platform Production SaaS processing 10K+ documents with RAG, multi-LLM orchestration, real-time streaming, and enterprise billing. Sub-2s response times, 99.9% uptime.

AI-First Full-Stack Engineer building production LLM systems. 3 years shipping RAG architecture, multi-model orchestration, real-time AI. Open to remote roles.

Production-ready ML model predicting DoorDash delivery times.

Using machine learning and applied analytics to identify high-residual opioid prescribers

Production-ready AI/ML code patterns for Claude, GPT & Gemini - 590 Python snippets, 264 Mermaid diagrams, 99.3% quality with LLM-optimized context

DeepFake detection with Deep Tree Network (DTN) architecture - 94.5% accuracy, 45 FPS real-time processing with zero-shot learning

Production-ready PyTorch framework for distributed deep learning training with Ray & Horovod backends. Optimized for computer vision and time series on Kubernetes clusters.