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.

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.

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

🍏 Discover the best Mac apps, tools, and resources to boost your productivity and streamline your workflow.

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

Production-ready ML model predicting DoorDash delivery times.