Explore a comprehensive presentation on implementing MLOps, drawing from real-world experience with a global Data & Analytics division. Gain insights into productionizing ML solutions and establishing organizational standards for success. Learn about key MLOps concepts, benefits, and common challenges when refactoring experimentation use-cases. Discover an Engagement Model addressing People, Processes, and Tools, including managing siloed data science demand, documentation practices, team structures, and tool requirements. Understand how to shift thinking towards operationalization and establish a Global MLOps Framework. Acquire valuable takeaways and learnings to enhance your organization's MLOps practices.
Overview
Syllabus
Intro
Thinking must shift to embrace operationalization
Establishing a Global MLOps Framework
Key Takeaways & Learnings
Processes
Tools
Taught by
Databricks
