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.

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

🛰️ 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

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.

Reproducible diagnostic investigation of a fine-tuned SLM that scored 99.75% on evaluation and failed silently on 10% of production inputs. Full pipeline. Every number verified.

Production-style end-to-end machine learning pipeline with modular architecture, experiment tracking, FastAPI inference, Docker, and CI/CD

800+ real-world ML & LLM system design case studies from 150+ companies Google, Meta, Netflix, Uber, Airbnb & more. Production AI, not theory.

Production ML template with 32 encoded anti-patterns, multi-cloud K8s, agent rules (AUTO/CONSULT/STOP), and supply-chain security for Windsurf, Claude Code, and Cursor.

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 stroke risk assessment platform powered by Dense Stacking Ensemble (DSE) ML models. Achieves 95-97% accuracy with real-time predictions.