This repository contains a structured course on machine learning operations (MLOps), covering the full lifecycle from experimentation to production deployment. The curriculum covers topics including data engineering, model training with PyTorch, distributed ML with Ray, LLMs, CI/CD workflows for ML, and observability. It provides hands-on Jupyter notebooks and code examples for building reliable, scalable ML systems.