Advanced Topics in Management Analytics: Machine Learning Operations

This course is part of Smith's 'Advanced Topics in Management Analytics' series and introduces MLOps, the practice of merging data science with software engineering practices specific to machine learning solutions. The course is delivered over four sessions for 28 contact hours and includes three team assignments, a team case study, and an individual assignment, all with a heavy emphasis on practical application. MLOps streamlines the tools and processes for the end-to-end (E2E) machine learning lifecycle by providing a unified set of ownership during the various steps in the pipeline. Students learn how to address the needs of different stakeholders and partners during multiple stages of the pipeline. Who is responsible for each step? How do you pivot to the next stage? Students assess the technical stack of the organization and identify gaps to improve deliverability. Lastly, participants achieve a high-level understanding of all the moving parts in a production-grade environment. The course prepares its participants to develop real-world solutions by providing first-principles thinking on implementing and evaluating machine learning solutions.