TECH 103 — The AI Leadership Series: Building and Scaling Solutions
Quarter: Spring
Instructor(s): Hamza Farooq
Date(s): Apr 2—May 21
Class Recording Available: Yes
Class Meeting Day: Wednesdays
Grade Restriction: No letter grade
Class Meeting Time: 7:00—8:30 pm (PT)
Tuition: $460
Refund Deadline: Apr 4
Unit(s): 1
Enrollment Limit: 50
Status: Closed
Quarter: Spring
Day: Wednesdays
Duration: 8 weeks
Time: 7:00—8:30 pm (PT)
Date(s): Apr 2—May 21
Unit(s): 1
Tuition: $460
Refund Deadline: Apr 4
Instructor(s): Hamza Farooq
Grade Restriction: No letter grade
Enrollment Limit: 50
Recording Available: Yes
Status: Closed
AI has the power to transform industries, but taking a concept from prototype to production is riddled with complex challenges. What crucial hurdles must be overcome to turn promising AI prototypes into production-ready solutions? This course equips you with the tools to identify these hurdles, overcome them, and scale AI solutions for real-world applications. Students will learn to navigate the entire AI lifecycle—from data processing and model training to deployment, monitoring, and optimization. Through practical exercises, you will explore how to balance technical and user experience needs by planning for both front-end and back-end requirements. By the end of the course, students will have a solid grasp of the end-to-end process involved in operationalizing AI solutions. You will be able to navigate the complexities of translating prototypes into scalable, production-ready AI applications that seamlessly integrate with existing systems and deliver optimal user experiences.
This is the final course of a three-course series. The first course, "Introducing AI Systems," was offered in Fall 2024. The second course, “Leading AI Transformation,” was offered in Winter 2025. Each course can be taken independently. No prerequisites or prior experience with AI are required.
HAMZA FAROOQ
Adjunct Professor, University of Minnesota and Santa Clara University
Hamza Farooq has over 15 years of experience leading data science and machine learning teams. His areas of expertise are experiment design, NLP, recommender systems, and time series forecasting. He was a senior research science manager at Google. Farooq received an MBA from Lahore University; an MS in business analytics and machine learning from the Carlson School of Management, University of Minnesota; and a BS in computer science from the National University of Computer and Emerging Sciences, Pakistan. Textbooks for this course:
(Required) Hamza Farooq, Build LLM Applications (from Scratch) (ISBN 978-1633436527)