Overview

This table will be updated weekly with links to course materials (lecture handouts, lab manuals) as we progress through the course.

Week	Lecture slides	Lab	Deadlines
1	ML systems	Hello, Chameleon Hello, Linux	Due by 1/30; no submission required
2	Cloud computing	Cloud computing on Chameleon	Due by 2/13; submit in Gradescope
3	DevOps for ML systems	Released TBD	Will be due 4/29 in Gradescope
4	Model training at scale	Large-scale model training on Chameleon	Will be due 2/27 in Gradescope
5	Model training infrastructure and platform	Train ML models with MLFlow and Ray	Will be due 3/13 in Gradescope
6	Model serving	Model optimizations for serving Serving on edge devices System optimizations for model serving	Will be due 3/20 in Gradescope
7	Evaluation and monitoring	Offline evaluation of ML systems Online evaluation of ML systems Closing the feedback loop	Will be due 4/22 in Gradescope
8	Data	Persistent storage on Chameleon Batch data pipelines Online data pipelines	Will be due 5/6 in Gradescope