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Systems for AI Seminar
Last Updated: 2026-06-01 11:31:16
Abstract
The seminar covers core concepts and research developments in hardware/software infrastructure for large-scale AI model training and serving. During the seminar, students will present research papers in groups, based on a list of papers that will be provided at the beginning of the course.
Objective
Students will learn to: - Understand and analyze trends in AI applications and hardware and their implications for AI system design - Analyze and critique state-of-the-art AI systems presented in research papers - Lead and participate in technical discussions about research papers
Content
Topics include distributed system design for AI pre-training, fine-tuning, and inference, model lifecycle management and MLOps, training data management, and compound AI system design, including retrieval augmented generation (RAG), reinforcement learning (RL) feedback, and agentic AI workflows.
Resources
Literature
The research paper reading list will be posted on the course website and will include papers from conferences like OSDI, SOSP, MLSys, NeurIPS, ICML, VLDB, SIGMOD.
General Information
- Language
- English
- Levels
- MSC
- Frequency
- Yearly recurring
Examination
- Type
- graded semester performance
Registration & Places
- Max Places
- 24
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| seminar | Systems for AI Seminar |
|
2 h weekly |