AI 210 - Efficient Model Engineering and Deployment

This course provides an in-depth exploration of advanced techniques for optimizing machine learning models for real-world deployment. Students will learn strategies for model compression, edge Al implementation, and deployment using industry-standard frameworks such as Docker and Flask. Emphasis will be placed on developing models that are both computationally efficient and high-performing, ensuring adaptability across various deployment environments. Through hands-on projects and case studies, students will acquire the skills necessary to design, optimize, and deploy machine learning models that meet real-world constraints on performance and resource utilization.

Credits: 3

Class Level Sophomore Undergraduate
Program Undergraduate