Logistics

• Lectures: Mon/Wed 3:15 - 4:45pm PST. Class: 75% lectures, 25% tutorials.
• Location: Zoom links can be found on Canvas
• Office hours:
  • Megan: Mon 2 - 2:30pm PST
  • Chloe: Tue 8:30 - 9am PST
  • Chip: Wed 6 - 6:30pm PST
  • Kinbert: Tue 3 - 3:30pm PST
• Grading:
  • one final project to build an ML application (65%). We'll have a demo day to showcase all students' final projects. See last year projects here
  • two to three fun, short assignments (30%)
  • discussion participation in class + EdStem + OHs (5%)
• Contact: Students should ask all course-related questions on our Piazza forum, where you will also find all the announcements.
• Academic accommodations: If you need an academic accommodation based on a disability, you should initiate the request with the Office of Accessible Education (OAE). The OAE will evaluate the request, recommend accommodations, and prepare a letter for faculty. Students should contact the OAE as soon as possible since timely notice is needed to coordinate accommodations.
• Honor code: Very important. See Honor Code.

Overview

What is this course about?

This course aims to provide an iterative framework for developing real-world machine learning systems that are deployable, reliable, and scalable.

It starts by considering all stakeholders of each machine learning project and their objectives. Different objectives require different design choices, and this course will discuss the tradeoffs of those choices.

Students will learn about data management, data engineering, feature engineering, approaches to model selection, training, scaling, how to continually monitor and deploy changes to ML systems, as well as the human side of ML projects such as team structure and business metrics. In the process, students will learn about important issues including privacy, fairness, and security.

Why machine learning systems design?

Machine learning systems design is the process of defining the software architecture, infrastructure, algorithms, and data for a machine learning system to satisfy specified requirements.

The tutorial approach has been tremendously successful in getting models off the ground. However, the resulting systems tend to go outdated quickly because (1) the tooling space is being innovated, (2) business requirements change, and (3) data distributions constantly shift. Without an intentional design to hold all the components together, a system will become technical liability, prone to errors and quick to fall apart.

Prerequisites

Students are expected to have the following background:

• Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program (e.g., CS106B/X or equivalent).
• Good understanding of machine learning algorithms (e.g. at least one of CS229, CS230, CS231N, CS224N or equivalent).
• Familiar with at least one framework such as TensorFlow, PyTorch, JAX.
• Familiarity with basic probability theory (CS109 or Stat116 or equivalent is sufficient but not necessary).

Honor Code

Permissive but strict. If unsure, please ask the course staff!

• OK to search, ask in public about the systems we’re studying. Cite all the resources you reference.
  E.g. if you read it in a paper, cite it. If you ask on Quora, include the link.
• NOT OK to ask someone to do assignments/projects for you.
• OK to discuss questions with classmates. Disclose your discussion partners.
• NOT OK to copy solutions from classmates.
• OK to use existing solutions as part of your projects/assignments. Clarify your contributions.
• NOT OK to pretend that someone’s solution is yours.
• OK to publish your final project after the course is over (we encourage that!)
• NOT OK to post your assignment solutions online.

Audit policy

We’re open to auditing requests by Stanford students and staff. You will be able to attend all the lectures, but we won't be able to grade your homework or give advice on final projects. Our human resources are limited. To audit the class, please send cs329s-win2022-staff@lists.stanford.edu an email with the subject title "CS329S: Audit Request" with a few sentences introducing yourself and your relevant background.

Because the course is in-person on campus, external requests will not be considered.

The slides, (very intensive) notes, assignments, and final project instructions will be made publicly available on the Syllabus page.

Reference Text

The course relies on lecture notes and accompanying readings.