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 analysis with ranking metrics, precision-recall tradeoffs, and conversion modeling.

One library, four surfaces. Production-grade Kolmogorov-Arnold Networks || TensorFlow + PyTorch + ONNX. || A small KAN beats a 10× larger MLP on smooth, separable target. One library. Two backends. Real ONNX export. Docker + Kubernetes ready.

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.

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

Sample code for improving multi-tool AI agents using SFT distillation and reinforcement fine-tuning — Qwen3-32B RFT achieving 86.9% quality on a retail customer service agent. From Microsoft Build 2026.

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

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

Detect prompt regressions before they reach production — per-category accuracy scoring, deterministic validation, and False Improvement detection. Pure Python, zero dependencies.

🛰️ 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.

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

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

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.

A practical AI/ML engineering playbook for real-data-first, baseline-driven, production-aware AI/ML projects.

End-to-end MLOps pipeline with Airflow ETL orchestration, Redis feature store, and real-time ML monitoring using Prometheus & Grafana with automated data drift detection

Production ML template with 38 encoded anti-patterns, multi-cloud K8s, agent rules (AUTO/CONSULT/STOP), and supply-chain security for Windsurf, Claude Code, and Cursor.