Software Tools and Techniques for AI

CS 203 · IIT Gandhinagar · Semester II, 2025–26

CS 203 covers the complete software engineering stack for modern AI development. While most machine learning courses focus on algorithms, this one focuses on the engineering practices that make ML systems work in the real world, from data collection to production deployment.

Instructor
Prof. Nipun Batra, Sustainability Lab
Credits
L 1.5 · T 0 · P 3 · C 4
Prerequisites
ES 112 (Computing) required; basic ML concepts preferred
Recordings
Course playlist
Discussion
Course Slack, for all announcements and discussion

Lectures

Course playlist All 13 lectures

Learning outcomes

By the end of the course, you will be able to:

Build end-to-end ML systems
From data collection to production deployment.
Choose the right tools
Know which tool to use for any data or ML task.
Write production code
Code that others can understand, maintain and reproduce.

The course runs over 15 weeks in three parts: data engineering (weeks 1–5: data collection, validation, labelling, augmentation), model development (weeks 6–11: LLM APIs, training, reproducibility, deployment, CI/CD) and production (weeks 12–15: edge deployment, profiling, monitoring).

Questions

How is the course graded?

Lab assignments with a viva 48%, quizzes 42% and attendance 10%. See Grading.

How many quizzes count?

There are four quizzes and your best three count. Quiz 4 is held in the end-semester examination period. See Quizzes.

What if I miss a class?

There is no leave request system: missed classes count as absences, and up to six absences cost nothing. For an emergency that keeps you away for four or more consecutive classes, discuss it with the instructor. See Attendance.

What are the prerequisites?

ES 112 (Computing) is required. Basic machine learning concepts are preferred.

Where are the slides, labs and recordings?

In the lecture list: every lecture links its slides, cheat sheet, recording and the lab that followed it. The course materials site has the full set, and the course playlist has the recordings.

Anything else: ask on the course Slack.

References

  • Kevin P. Murphy, Probabilistic Machine Learning: An Introduction, MIT Press, 2022
  • Kevin P. Murphy, Probabilistic Machine Learning: Advanced Topics, MIT Press, 2022
  • Noah Gift and Alfredo Deza, Practical MLOps, O’Reilly, 2021
  • Ron Kohavi, Diane Tang and Ya Xu, Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing
  • Aurélien Géron, Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, O’Reilly, 2022
  • Stanford CS 329S