✨ Build a machine learning model from a prompt

Cryptocurrency trading engineering: A scalable back testing infrastructure and a reliable, large-scale trading data pipeline

ML Engineering best practices | ML model deployment as a FastAPI service, containerized with Docker for scalability and reproducibility.

ML Engineering best practices | Deploying an ML API with FastAPI, PostgreSQL, and Docker for scalable model storage and inference.

Enterprise reference architecture for Agentic AI systems using LangGraph, RAG, knowledge graphs, and LLM reasoning to power autonomous operational decision workflows.

It is easy to prototype ML models. With higher levels of abstraction, the expertise required to build any ML model is decreasing each day.

Repository for project work of Udacity's Azure ML Engineer nanodegree.