Machine Learning Systems Design


Stanford School of Engineering

Course image for Machine Learning Systems Design


To be considered for this course, please enroll and submit an application. We will let you know by Dec. 11 whether you have been admitted into the course.


This project-based course covers the iterative process for designing, developing, and deploying machine learning systems. It focuses on systems that require massive datasets and compute resources, such as large neural networks. Students will learn about the different layers of the data pipeline, approaches to model selection, training, scaling, as well as how to deploy, monitor, and maintain ML systems. In the process, students will learn about important issues including privacy, fairness, and security.


  • At least one of the following; CS229, CS230, CS231N, CS224N or equivalent.
  • Students should have a good understanding of machine learning algorithms
  • Students should be familiar with at least one framework such as TensorFlow, PyTorch, JAX
001 Winter 2020-21 Online

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Dates:January 11 - March 19, 2021
Units: 3.00-4.00
Instructors: Chip Huyen
Delivery Option:
For Credit $4,056.00-$5,408.00
Notes: Pre-registration Dates: November 2, 2020 at 9:00am to December 4, 2020 at 5:00pm

Computer Science Department Requirement
Students taking graduate courses in Computer Science must enroll for the maximum number of units and maintain a B or better in each course in order to continue taking courses under the Non Degree Option.

Pre-registration for this course will secure your enrollment request and ensure timely processing of your application for potential course approval. Please note: course enrollment will be confirmed after December 11, 2020; after completing your pre-registration, no further action is required on your part.


This course may not currently be available to learners in some states and territories.