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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, user-scalable=no, initial-scale=1">
<title>CS 329S | Home</title>
<!-- bootstrap -->
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css">
<!-- Google fonts -->
<link href='http://fonts.googleapis.com/css?family=Roboto:400,300' rel='stylesheet' type='text/css'>
<link rel="stylesheet" type="text/css" href="style.css" />
</head>
<body>
<script src="header.js"></script>
<!-- Logistics -->
<div class="sechighlight">
<div class="container sec" id="logistics">
<div class="alert alert-danger" role="alert">
We love the students' work this year! You can find recording of the demo day on <a
href="https://www.youtube.com/watch?v=AZNTqytOhXk&t=12771s">YouTube</a>!<br>
Lecture notes for the course have been expanded into the book <a
href="https://www.amazon.com/Designing-Machine-Learning-Systems-Production-Ready/dp/1098107969">Designing Machine Learning Systems</a>
(Chip Huyen, O'Reilly 2022).
</div>
<h2>Logistics</h2>
<ul>
<li><b>Lectures</b>: Mon/Wed 3:15 - 4:45pm PST. Class: 75% lectures, 25% tutorials.</li>
<li><b>Location</b>: Zoom links can be found on Canvas</li>
<li><b>Office hours</b>:
<ul>
<li>Megan: Mon 2 - 2:30pm PST</li>
<li>Chloe: Tue 8:30 - 9am PST</li>
<li>Chip: Wed 6 - 6:30pm PST</li>
<li>Kinbert: Tue 3 - 3:30pm PST</li>
</ul>
</li>
<li><b>Grading</b>:
<ul>
<li>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 <a
href="https://stanford-cs329s.github.io/reports/">here</a></li>
<li>two to three fun, short assignments (30%)</li>
<li>discussion participation in class + EdStem + OHs (5%)</li>
</ul>
</li>
<li><b>Contact</b>: Students should ask <i>all</i> course-related questions on our Piazza forum, where
you will also find all the announcements.</li>
<li><b>Academic accommodations</b>: If you need an academic accommodation based on a disability, you
should initiate the request with the <a
href="https://oae.stanford.edu/accommodations/academic-accommodations">Office of Accessible
Education (OAE)</a>.
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.</li>
<li><b>Honor code</b>: Very important. See <a href="#honor-code">Honor Code</a>.</li>
</ul>
</div>
</div>
<div class="container sec">
<!-- Staff Info -->
<h2>Team</h2>
<!-- <ul>
<li><b>Instructor</b>: <a href="https://huyenchip.com">Chip Huyen</a> </li>
<li><b>TA</b>: <a href="https://krandiash.github.io/">Karan Goel</a>++</li>
<li><b>Advisors</b>: <a href="https://pirroh.fyi">Michele Catasta</a>, <a href="https://cs.stanford.edu/~chrismre/">Christopher Ré</a></li>
</ul> -->
<div class="row">
<div class="col-md-2">
<h3>Instructor</h3>
<div class="instructor">
<a href="https://huyenchip.com">
<div class="instructorphoto"><img src="images/chip.jpg"></div>
<div>Chip Huyen</div>
</a>
</div>
</div>
<div class="col-md-6">
<h3>TAs</h3>
<div class="instructor">
<a href="http://www.mleszczy.com">
<div class="instructorphoto"><img src="images/megan.jpeg"></div>
<div>Megan Leszczynski</div>
</a>
</div>
<div class="instructor">
<a href="https://www.linkedin.com/in/kinbertchou/">
<div class="instructorphoto"><img src="images/kinbert.jpeg"></div>
<div>Kinbert Chou</div>
</a>
</div>
<div class="instructor">
<a href="https://www.chloe-he.com/">
<div class="instructorphoto"><img src="images/chloe.jpeg"></div>
<div>Chloe He</div>
</a>
</div>
</div>
</div>
</div>
<!-- Content -->
<!-- Note the margin-top:-20px and the <br> serve to make the #content hyperlink display correctly (with the h2 header visible) -->
<div class="sechighlight">
<div class="container sec" id="overview" style="margin-top:-20px"><br>
<h2>Overview</h2>
<h3><i class="fas fa-bolt"></i> What is this course about?</h3>
<p>
This course aims to provide an iterative framework for developing real-world machine learning systems
that are deployable, reliable, and scalable.<br><br>
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.<br><br>
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.
<br>
</p>
<h3><i class="fas fa-bullseye"></i> Why machine learning systems design?</h3>
<p>
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.<br><br>
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.
</p>
<h3><i class="fas fa-cogs"></i> Prerequisites</h3>
<p>Students are expected to have the following background:</p>
<ul>
<li>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).</li>
<li>Good understanding of machine learning algorithms (e.g. at least one of CS229, CS230, CS231N, CS224N
or equivalent). </li>
<li>Familiar with at least one framework such as TensorFlow, PyTorch, JAX.</li>
<li>Familiarity with basic probability theory (CS109 or Stat116 or equivalent is sufficient but not
necessary). </li>
</ul>
<h3 id="honor-code"><i class="fas fa-eye"></i> Honor Code</h3>
<p>Permissive but strict. If unsure, please ask the course staff!</p>
<ul>
<li>OK to search, ask in public about the systems we’re studying. Cite all the resources you
reference.<br>
E.g. if you read it in a paper, cite it. If you ask on Quora, include the link.</li>
<li>NOT OK to ask someone to do assignments/projects for you.</li>
<li>OK to discuss questions with classmates. Disclose your discussion partners.</li>
<li>NOT OK to copy solutions from classmates.</li>
<li>OK to use existing solutions as part of your projects/assignments. Clarify your contributions.</li>
<li>NOT OK to pretend that someone’s solution is yours.</li>
<li>OK to publish your final project after the course is over (we encourage that!)</li>
<li>NOT OK to post your assignment solutions online.</li>
</ul>
<h3 id="honor-code" id="audit"><i class="fas fa-user-plus"></i> Audit policy</h3>
<p>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 [email protected] an email
with the subject title "CS329S: Audit Request" with a few sentences introducing yourself and your
relevant background.
<p>
<p>Because the course is in-person on campus, external requests will not be considered.</p>
<p>The slides, (very intensive) notes, assignments, and final project instructions will be made publicly
available on the Syllabus page.
<h3><i class="fas fa-book"></i> Reference Text</h3>
<p>
The course relies on lecture notes and accompanying readings.<br><br>
<!-- <b>Relevant books to the course</b> (not official textbooks):
<ul>
<li><a href="https://learning.oreilly.com/library/view/designing-machine-learning/9781098107956/">Designing Machine Learning Systems</a> (Chip Huyen, O'Reilly '22).</li>
<li><a href="">Reliable Machine Learning</a> (Chen et al., O'Reilly '22')</li>
</ul> -->
</p>
</div>
</div>
<div class="container sec" id="faq" style="margin-top:-20px"><br>
<h2>FAQ</h2>
<div class="qqa">
<div class="qq">How difficult is the course?</div>
<div class="qa">
The materials are not difficult to understand, but the final projects are fairly involved. We wouldn't
recommend taking the course unless you're ready to build things and learn from hands-on experience!
</div>
</div>
<div class="qqa">
<div class="qq">Does the course count towards CS degrees?</div>
<div class="qa">
For undergraduates, CS 329S can be used as a Track C requirement or a general elective for the AI track.
For all other tracks, they would need to petition to use the course.<br><br>
For master's students, CS 329S can satisfy the AI Specialization Depth C requirement. It can also be
used as a general elective for all MS students, regardless of their specialization.
</div>
</div>
<div class="qqa">
<div class="qq">Are lectures recorded?</div>
<div class="qa">Yes, the lectures will be recorded and made available to enrolled students, including SCPD
students.</div>
</div>
<div class="qqa">
<div class="qq">Is attendance mandatory?</div>
<div class="qa">We won't be taking attendance but we expect to see you often in class. We love talking to
students to understand how you are doing, make sure you get the most out of the class, and get your
feedback to improve the materials. The class is relatively small so we will probably get to know each
other well.<br>
If you have a time conflict and can't attend the lectures, please send us an email to let us know!
</div>
</div>
<div class="qqa">
<div class="qq">What is the format of the class?</div>
<div class="qa">It will be lectures, tutorials, and discussion. We will often have industry experts to give
us tutorials on tools for data streaming, experiment tracking, deep learning framework, monitoring, etc.
</div>
</div>
<div class="qqa">
<div class="qq">I don't have a team for the final project, can I still enroll?</div>
<div class="qa">Yes. Most students don't have a team already when they join the course. We'll have
activities for you to find project partners.</div>
</div>
<div class="qqa">
<div class="qq">Can I work in groups for the assignments?</div>
<div class="qa">Yes, in groups of up to two people.</div>
</div>
<div class="qqa">
<div class="qq">How mature is the course?</div>
<div class="qa">
This is the second time the course is offered. The materials are a lot more developed compared to the
first time, however, there is still a long way to go. We're trying our best to ensure the quality of the
lectures, but here and there things might not be as polished as other courses. Your feedback will be
greatly appreciated.
</div>
</div>
<div class="qqa">
<div class="qq">Do I need to know Python for the course?</div>
<div class="qa">Since Python has become the most popular language for machine learning, we expect most
tutorials will be in Python. Python fluency isn't required, but will make your life so much easier
during the course.
</div>
</div>
<div class="qqa">
<div class="qq">Will the videos be made available publicly?</div>
<div class="qa">We won't know untile the end of the course. We're trying, but it's not solely our decision
to make. We'll announce on the <a
href="http://mailman.stanford.edu/mailman/listinfo/cs329s-win2021-guests">guest mailing list</a> if
and when the videos are available.</div>
</div>
<div class="qqa">
<div class="qq">Can I follow along from the outside?</div>
<div class="qa">We'd be happy if you join us! All the slides and lecture notes will be posted on this
website. You can also subscribe to the <a
href="http://mailman.stanford.edu/mailman/listinfo/cs329s-win2021-guests">guest mailing list</a> to
get updates from the course. </div>
</div>
<div class="qqa">
<div class="qq">I have a question about the class. What is the best way to reach the course staff?</div>
<div class="qa">Please post your question on the <a href="https://edstem.org/us/courses/16258/">course
forum</a> so that other students can benefit from your questions. If you have a personal matter or
emergencies, please email the staff at [email protected].</div>
</div>
</div>
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