Información del curso

This advanced course focuses on the system and data management challenges in managing and serving machine learning (ML) models. The course starts by recapping standard ML pipelines up to the training stage. The lifecycle of an ML model extends far beyond training. Students will be introduced to foundational frameworks for model management, where trained models are treated as core data artifacts. The course covers data management techniques and optimizations, including model selection, versioning, lineage tracking, and metadata management. Building on this, students will explore the architectures of modern model serving systems, along with performance optimizations such as dynamic batching, model compilation, and resource-efficient inference execution. Furthermore, students will be exposed to state-of-the-art research on inference and model management. Through a combination of seminal research papers and hands-on projects, students will gain a comprehensive understanding of the entire model lifecycle beyond training, preparing them for both academic research and real-world system design.