CS 5470

Systems for Large-Scale ML

Course Description

Course information provided by the 2025-2026 Catalog.

Systems for Large-scale ML is a new advanced course at Cornell University designed to equip students with hands-on expertise in designing and operating scalable machine learning systems. With the rise in popularity of large ML models like GPT, LLaMA, and DeepSeek, tackling systems-level challenges of distributing training and inference workloads on multi-accelerator hardware while ensuring fault tolerance has become a crucial skill for both graduate and undergraduate students in computer science. The course will teach students to tackle systems challenges in both training and inferring from large-scale ML models. We will combine theory and hands-on teaching through lectures, assignments, and projects.

Last 3 terms offered (None)

Learning Outcomes REF-FA25

• Distribute the training and inference of large ML models across multiple GPUs.
• Build efficient strategies for sharding ML models.
• Debug communication overheads of distributed ML.
• Develop fault tolerant and elastic ML pipelines.

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