Machine Learning System Design

COM SCI X 450.43

This course provides an in-depth exploration of machine learning systems design, covering the complete lifecycle from project scoping and data acquisition to model deployment and monitoring. We connect theoretical…

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What you can learn.

Describe core principles and practices of MLOps
Build production-ready machine learning systems
Evaluate ML system designs for real-world applications
Design complete ML pipelines from data to deployment

About this course:

This course provides an in-depth exploration of machine learning systems design, covering the complete lifecycle from project scoping and data acquisition to model deployment and monitoring. We connect theoretical foundations with practical application, emphasizing that as foundation (pre-trained) models become more sophisticated, human judgment becomes more critical for the successful implementation of machine learning in production. This course focuses on fundamental, tool-agnostic principles and industry best practices. You will learn to make strategic decisions regarding data quality, system architecture, model selection, and safety to effectively integrate AI/ML applications into business and technological contexts.

Prerequisites

Machine Learning Using Python or Machine Learning Using R