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

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

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

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

Here I show my projects and my wiki to learn everything you need to know about ML, among others.