Machine Learning in Practice

Catalog description

As Machine Learning and Artificial Intelligence methods have become common place in both academic and industry environments the majority of resources have focused on methods and techniques for applications. However, there are many considerations that must be addressed when deploying such techniques into practice (or production). The purpose of this course is to cover topics relevant to building a machine learning systems deployed into operations. Such systems have technical requirements including data management, model development, and deployment. However, business/organizational impacts must also be considered. Machine learning systems can be expensive to produce and operate. Students will learn about trade-offs in design, implementation, and expected value. After completing this course, students will: 1. Have the ability to deploy produces with machine learning and AI components; 2. Understand how to implement data pipelines and data engineering systems; 3. Calculate the approximate value provided by a machine learning system to an organization; 4. Understand how to continually assess the value and quality of a deployed machine learning system. Prerequisites: understanding of basic machine learning concepts (i.e. supervised/unsupervised learning). This is a graduate level course. Prerequisite: 17-634 Min. grade B