Software Tools and Techniques for AI

CS 203 | Jan–Apr 2026 | IIT Gandhinagar

Instructor: Dr. Nipun Batra, Sustainability Lab Credits: L1.5 T0 P3 C4

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Overview

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

Learning Outcomes

By the end of this course, you will:

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

Course Structure (15 Weeks)

Part Weeks Topics
Data Engineering 1–5 Data collection, validation, labeling, augmentation
Model Development 6–11 LLM APIs, training, reproducibility, deployment, CI/CD
Production 12–15 Edge deployment, profiling, monitoring

Grading

Component Weight
Lab Assignments + Viva 48%
Quizzes (best 3 of 4) 42%
Attendance 10%

Attendance

Absences Grade
≤ 6 10%
7–8 9%
9 8%
10 7%
11 5%
12–14 1%
> 14 0%

No leave request system. Missed classes count as absences. For emergencies (4+ consecutive classes), discuss with the instructor.

Prerequisites

  • Required: ES112 (Computing)
  • Preferred: Basic ML concepts

References

  1. Murphy, K.P. Probabilistic Machine Learning: An Introduction. MIT Press, 2022.
  2. Murphy, K.P. Probabilistic Machine Learning: Advanced Topics. MIT Press, 2022.
  3. Gift & Deza. Practical MLOps. O’Reilly, 2021.
  4. Kohavi, Tang, Xu. Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing.
  5. Géron. Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow. O’Reilly, 2022.
  6. Stanford CS 329S