AI-839

ML Production Engineering

Welcome

Hello Students of AI 839.

See the course page for recent information on Lectures, Homeworks, Projects, etc..

Announcements

  • [30-November-2024] COURSE ENDS
  • [30-November-2024] Project Presentations
  • [29-November-2024] Project Presentations
  • [26-November-2024] Machine Unlearning W16-L02 added to course page
  • [26-November-2024] Fairness and Bias W16-L01 added to course page
  • [19-November-2024] Meta Learning lecture page W15-L01 added to course page
  • [15-November-2024] Data Labelling guest lecture deck added to course page
  • [13-November-2024] AI Security guest lecture deck added to course page
  • [07-November-2024] Lecture page added to W13-L01, W13-L02
  • [25-October-2024] Lecture page added to W11-L02
  • [22-October-2024] Dr. Amit’s Causal ML deck added to course page.
  • [22-October-2024] Guest lecture decks added.
  • [01-October-2024] Generic feedback on HWs is added to homeworks page.
  • [30-September-2024] Midterm Bonus Problem released. Due by 11.59pm, Oct 15, 2024 IST.
  • [27-September-2024] Midterm at 11.15am, Tue, Sept, 2024 IST.
  • [17-September-2024] HW-05 extended to 11.59pm, Tue, Sept, 2024 IST.
  • [16-September-2024] Notes added to W08-L01. Updated course page with scheudle for the rest of the course (tentative).
  • [12-September-2024] Notes added to W07-L01. W07-L02 is added.
  • [05-September-2024] Homework, Minor and Major preoject details added. See HW-05, HW-06, Minor Project, Major Project for details. Course pages updated.
  • [05-September-2024] Lecture Page W05-L01, W05-L02, W06-L01, W06-L02 added. Course pages updated.
  • [25-August-2024] Lecture Page W03-L02, W04-L01, W04-L02 added. Course pages updated.
  • [23-August-2024] HW-03 and HW-04 added.
  • [13-August-2024] Lecture Page W03-L01 added. Course pages updated.
  • [09-August-2024] Lecture Page W02-L02 added. Course pages updated. HW-02 added
  • [06-August-2024] Lecture Page W02-L01 added. Project Card, as a jupyter notebook is added.
  • [01-August-2024] Course website up, Lecture Page W01-L01 added. HW-01 added.

Overview

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

  • Topics
    • basic principles and MLOps with Open Source Software
    • three assignments
  • 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 metrics
    • one assignment, one mini project and a midterm
  • 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.

Part-3: Intro to LLM(Ops) & Application

  • Topics
    • practice, cloud solutions
    • capstone project and presentations
    • invited lectures from Industry
  • Learning Outcomes: students will be able to
    • frame, discover, develop, deploy, monitor, improve, re-deploy and maintain an ML Application
    • approach the problem holistically, optimize RoI