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.
Π Π΅ΠΏΠΎΠ·ΠΈΡΠΎΡΠΈΠΉ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ Production ML, Π²Π΅ΡΠ½Π° 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
π°οΈ 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
Complete AI/ML curriculum: From Python basics to production systems. 800+ notebooks covering transformers, embeddings, RAG, vector DBs, MLOps, NLP, computer vision & more.
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.
AI-First Full-Stack Engineer building production LLM systems. 3 years shipping RAG architecture, multi-model orchestration, real-time AI. Open to remote roles.
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.
Production-ready ML model predicting DoorDash delivery times.
DeepFake detection with Deep Tree Network (DTN) architecture - 94.5% accuracy, 45 FPS real-time processing with zero-shot learning
Production-ready stroke risk assessment platform powered by Dense Stacking Ensemble (DSE) ML models. Achieves 95-97% accuracy with real-time predictions.
Production-ready AI/ML code patterns for Claude, GPT & Gemini - 590 Python snippets, 264 Mermaid diagrams, 99.3% quality with LLM-optimized context
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"
π Discover the best Mac apps, tools, and resources to boost your productivity and streamline your workflow.
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