From ML fundamentals to production deployment, everything you need to become a production ML engineer
-
Updated
Nov 26, 2025 - Shell
From ML fundamentals to production deployment, everything you need to become a production ML engineer
Enterprise-ready RAG template: semantic chunking, NLI hallucination detection, latency budgeting, Prometheus observability. Battle-tested at scale.
End-to-end behavioral prediction system using digital phenotyping. PyTorch Transformer (MAE 1.18) + Autoencoder anomaly detection. Docker-ready, FastAPI service.
Production RAG system for automated enterprise support using Vertex AI embeddings, Neo4j knowledge graphs, and LangChain/LangGraph agentic workflows. Achieves 95%+ accuracy through semantic search, multi-hop reasoning, and confidence-based escalation with comprehensive evaluation frameworks.
Add a description, image, and links to the production-ml-systems topic page so that developers can more easily learn about it.
To associate your repository with the production-ml-systems topic, visit your repo's landing page and select "manage topics."