AI Systems and Tools

Catalog description

The goal of this course is to provide students with an understanding and overview of practical hardware and software system considerations in reliable and scalable development and deployment of modern machine learning (ML) and artificial intelligence (AI) models. The course imparts the essential techniques for designing algorithms that are mindful of software and hardware constraints. It equips learners with the key techniques and best practices for seamlessly deploying AI models across the entire lifecycle, from training to deployment and monitoring. Through this comprehensive approach, students gain invaluable skills for efficiently translating AI models into real-world applications, ensuring optimal performance and resource utilization. By investigating the fundamental differences between AI/ML as a model versus AI/ML as a system deployed in production, students' learning of these practical concepts, techniques, and methods will be facilitated by exemplary programming demonstrations and coding exercises so that the students can translate AI system design principles into practical machine learning applications and understand the fundamental differences between AI/ML as a model versus AI/ML as a system deployed in production.