CS 329S: Machine Learning Systems Design

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.

Link: https://stanford-cs329s.github.io/index.html#overview

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General hashtags: #mlsystemdesign #systemdesign #machinelearningsystemdesign #machinelearning #algorithms #design #architecture #engineering #software

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