07/22/2025 MLOps: Best Practices for Delivering and Managing Machine Learning
July 22, 2025
9:00 am - 12:30 pm CT
Half-Day (Morning)
Prerequisite: None
Deanne Larson, Ph.D.
DM, CBIP, President
Larson & Associates
Deanne Larson, Ph.D., is an active data science practitioner and academic. Her research has focused on enterprise data strategy, agile analytics, and data science best practices. She holds Project Management Professional (PMP), Project Management Agile Certified Practitioner (PMI-ACP), Certified Business Intelligence Professional (CBIP), and Six Sigma certifications. Deanne attended AT&T Executive Training at the Harvard Business School focusing on IT leadership, Stanford University focusing on data science, and New York University focusing on business analytics. She has presented at multiple conferences including TDWI, TDWI Europe, PMI, and other academic conferences. She is a faculty member at Purdue Global, has consulted for several Fortune 500 companies, and has authored multiple research articles on data science methodology and best practices.
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