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

Personal GitHub profile showcasing expertise in AI/ML engineering, generative AI, data science, and scalable production-ready solutions.

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.

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 pipeline for regression tasks with modular architecture (0.94 R², Kaggle validated)

Production-ready ML model predicting DoorDash delivery times.

Built on peer-reviewed research accepted at IEEE DSA 2025: "Hamiltonian Neural Networks for Robust Out-of-Time Credit Scoring: Empirical Validation and Temporal Stability Analysis"

CIPS is a regression-based ML model that predicts the final score of a cricket team batting second in limited-overs matches (like IPL). Using live match data (overs left, wickets fallen, run rate, etc.).

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