Lectures

Lecture recordings are available on YouTube.

Hands-On Tutorials are available on GitHub.

Detailed instructions for different stages of course projects are available here.

• Lecture 1: Reconciling Accuracy, Cost, and Latency of Inference Serving Systems
  tl;dr: This lecture reviews three related works out of AISys lab to set the context for the course and will be served as an example of MLSys research.
• Lecture 2: Machine Learning Systems: Course Overview
  tl;dr: This lecture reviews a brief overview of the course, its requirements, learning goals, policies, and expectations.
• Lecture 3: How to Read an MLSys Paper?
  tl;dr: In this lecture, we discuss a systematic approach for understanding, both high-level ideas and technical details in MLSys papers.
• Lecture 4: Designing and Motivating (ML) Systems Experiments
  tl;dr: This lecture offers students both theoretical understanding and practical guidance by using InferLine as a concrete example, while giving them a clear roadmap for how to motivate their own projects experimentally.
• Lecture 5: Machine Learning Systems in Production
  tl;dr: So far, we have explored two specific ML Systems work in research (InferLine and IPA)! This lecture contrasts challenges in building and deploying real-world ML systems in production vs in research.
• Lecture 6: Designing Machine Learning Systems
  tl;dr: This lecture discusses designing and building process of ML Systems.
• Lecture 7: Understanding and Explaining the Root Causes of (Performance) Faults in (ML) Systems with Causal AI
  tl;dr: This lecture discusses several research directions (multi-objective optimization, transfer learning, causal inference) in ML Systems.
• Lecture 8: Replicating Results in Machine Learning Systems Research
  tl;dr: This lecture discusses the importance of replication in machine learning systems research and how you can integrate it into your projects.
• Guest Lecture: Performance Modeling, Debugging, and Optimization of Highly Configurable Robotic Systems with Causal AI
  tl;dr: In this guest lecture, Abir Hossen (PhD student at AISys lab) talks about his research on finding root causes of failures in robotic systems using causal reasoning.
• Guest Lecture: The Effects of Interventions and Causal Bayesian Optimization
  tl;dr: In this guest lecture, Mohammad Ali Javidian (Appalachian State University) talks about his recent work on Causal BO.
• Lecture 10: Building the Next Impactful ML System: Lessons, Strategies, and Inspirations from CSCE 585
  tl;dr: This final lecture discusses how to build the next impactful ML Systems, drawing inspiration from current course materials.