CSC/EE 8001: Designing End-to-End ML Systems
Announcements
- [2025-08-29] Assignment 1 released on Canvas; see due date under Schedule.
- [2025-08-26] Office hours: Tuesday 5:00PM-6:00PM Naka 311. Please also send email if you plan to meet on Zoom.
Course Goals
This is a research-based course on systems for machine learning (ML), at the intersection of ML/AI, data management, and systems. Students will learn about the landscape and evolution of ML systems and current research. Topics include scalable model-building systems, data sourcing and preparation, ML platforms, deployment concerns, and MLOps. A major component is a project focused on MLOps and research paper reviews. The course is currently for MS and PhD students.
Schedule
| Date | Details | PDFs |
|---|---|---|
| Machine Learning (ML) and ML systems overview | — | |
| Loss functions, Gradient Descent, Bias-Variance, and ML development cycle | — | |
| Neural Networks and large-scale ML systems | — | |
| Cloud Computing and DevOps for ML | — | |
| Data Selection | — | |
| Model performance and model evaluation | — |