Explore the challenges and solutions in operationalizing machine learning systems in this 52-minute podcast episode featuring Nathan Ryan Frank, Director of Machine Learning Platform & Operations at WW Grainger. Dive into the ML development workflow, covering team dynamics, communication issues between roles, and approaching MLOps from an SRE/DevOps perspective. Gain insights on unique challenges in ML operationalization and practical guidance for those new to MLOps. Learn about Nathan's background in astrophysics and his journey into machine learning. Discover essential elements of ML workflows, the importance of testing, and strategies for bridging the language gap between stakeholders. Benefit from Nathan's expertise as he shares his approach to building machine learning systems and fostering team collaboration through shared language.
Challenges Operationalizing Machine Learning and Solutions
Overview
Syllabus
[] Nathan's preferred coffee
[] Takeaways
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[] Telescope for gamma-ray burst
[] Transition into ML
[] Stats-heavy US sports commentary
[] Building machine learning systems approach
[] ML Workflow Must-Haves
[] Love for tests
[] Test Writing Importance
[] Bridging Stakeholder Language Gap
[] Shared Language, Team Collaboration
[] Rapid fire questions
[] Wrap up
Taught by
MLOps.community
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