Level: Beginner to Intermediate
Prerequisite: None
Deanne Larson, Ph.D., DM, CBIP
President
Larson & Associates and TDWI Fellow
Dr. Deanne Larson is an active practitioner, educator, and author in AI, analytics, and project management. Her doctoral research examined how organizations establish enterprise data strategy using a grounded theory qualitative approach. She holds a Doctorate of Management, a Ph.D., and certifications including PMP, CBIP, and Six Sigma. Dr. Larson is also the author of Modern Business Analytics, published by O’Reilly, and regularly presents at conferences on AI, data management, analytics, and emerging technologies.
This is part of an optional week-long series.
Take all the courses to earn a certificate, or attend this individual course.
Machine learning operations (MLOps) is a process-focused and automated methodology for delivering advanced analytics. Building on the data management foundations of DataOps, MLOps provides end-to-end processes to develop, build, test, and automate machine learning and AI models. MLOps is an emerging practice that organizations want to adopt to decrease model time to market and create reproducible capabilities. This course addresses what MLOps is and how it differs from conventional software development, covers MLOps best practices, and prepares you to get started.
You Will Learn
- The definition and scope of MLOps
- Agile principles that are applied to MLOps projects
- How machine learning models and training sets are incorporated into the CI/CD process
- How MLOps reduces technical debt
- Why MLOps focuses on the automation of the machine learning process
- Best practices and how to get started with MLOps
Geared To
- Project managers
- Data scientists
- Data engineers
- Analysts
- Analytics managers