14-810 Special Topics

12 units, Carnegie Mellon University

The implementation of AI at large scale requires solid technical infrastructure to support its heavy, complex processes. In this course, students will learn to be effective users of AI systems. Students will gain handson experience with AIprocessing frameworks and infrastructure tools, in the context of large realworld data sets and under conditions requiring engineering design choices. Topics include Architecture design, data collection, data modeling and integration, data quality, data engineering, AImodel selection and optimization, model observability, endpoint architecture design, and deployment of the AImodel to the cloud.

Prerequisites: none listed

Courseweave needs JavaScript to show sections, workloads and planning tools.