Machine Learning Systems Design


Stanford School of Engineering

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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

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