Updates

• [03-January-2025] updated MLOps Stack page
• [01-January-2025] Material launched based on AI-839 taught at IIIT-B in the Fall of 2024.

Overview

• See preface for why MLOps and the approach and outlook taken here.
• See the Full Stack MLOps page for recent information on Lecture Notes, Homeworks, Projects, etc..

Prereqs

• Exposure and skill in data handling, building models in Python, PyTorch
• Exposure and skill in developing code using Python, Git, IDEs like VS Code
• A foundation course in Machine Learning, Deep Learning, Data Modeling, working with (Big) Data

Part-1: Essentials (ML Engineering)

• Topics
  • MLOps motivation, need
  • Basic principles and MLOps with Open Source Software
• Learning Outcomes: students will be able to
  • Deploy models with logging, documentation, unit tests, and APIs
  • Understand a conceptual framework to approach MLOps holistically

Part-2: Full Stack MLOps

• Topics
  • Holistic understanding of ML development, beyond chasing typical performance metric
• Learning Outcomes: students will be able to
  • deploy models, observe their performance, make improvements, redeploy them.
  • ensure that the ML pipeline is reproducible.
  • incorporate principles from Responsible AI and build ML systems which can consist of many models and tools.
  • frame, discover, develop, deploy, monitor, improve, re-deploy and maintain an ML Application