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Stanford University · Course

Systems for Machine Learning (CS229S)

Systems for Machine Learning (CS229S) on Stanford Online is a ten-week graduate course on the performance and scalability of deep learning systems, covering efficient training, fine-tuning and inference with an emphasis on Transformers and large language models.

Open at Stanford University ↗Common questions

Who it suits

  • Engineers who serve or train large models and care about cost and speed.
  • Programmers with Python and machine learning foundations who want the systems side of LLMs.

What it covers

  • Efficient training, fine-tuning and inference of deep learning models.
  • Performance hurdles across the stack: model complexity, data size, accelerator heterogeneity and network properties.
  • Transformer architectures and LLMs in particular.

Before you start

  • Stanford lists a conferred bachelor’s degree with an undergraduate GPA of 3.0 or better.
  • Programming in Python and NumPy, probability, multivariable calculus and linear algebra.
  • Foundations of machine learning at the level of CS221, CS229 or CS230.

Common questions about Systems for Machine Learning (CS229S)

How long is it?
Stanford lists 10 weeks at 15 to 25 hours a week, for 3 academic units.
Are there extra costs?
Stanford says students should expect to pay an additional amount in cloud credits for the final project. Check its page for the current figure.
Is it open for enrolment?
Stanford’s page said it was not open at the time of reading. Check the page for current availability.

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Where this comes from

Written from Stanford University’s own pages, read on 2026-10-14. Content, prices and availability change, so the provider’s page is the source. We are not affiliated with Stanford University and this is not an endorsement.