Nathan Ryan Frank is the Machine Learning Operations and platform Director of Grainger. Former Astrophysicist turned data scientist and machine learning engineer with a proven history of delivering results into production across a wide variety of domains while leading projects with international, cross-functional teams.
MLOps podcast #199 with Nathan Ryan Frank, Director, Machine Learning Platform & Operations at WW Grainger, Challenges Operationalizing Machine Learning (And Some Solutions).
// Abstract
This talk details some common challenges and pitfalls when attempting to operationalize machine learning systems and discusses some simple solutions. We dive into the machine learning development workflow and cover topics such as team dynamics, communication issues between roles that don't share a common language, and approaching MLOps from an SRE/DevOps perspective. Similarly, the talk highlights some challenges unique to operationalizing machine learning, drawing distinctions where necessary to highlight a large amount of similarity. Finally, the talk offers some simple and practical guidance for those new to MLOps who want to understand where to start and how to adopt best practices in an evolving field.
// Bio
Nathan Frank is currently the Director of Machine Learning Platform and Operations at Grainger where he is building a team to support the Technology Group's expanding machine learning efforts. Prior to joining Grainger, Nathan led machine learning engineering efforts at Strong Analytics, a boutique data science and machine learning consulting firm, as well as machine learning platform and development teams at Stats Perform, a leader in sports data and technology. Nathan holds bachelor's and master's degrees in Astrophysics from UC - Santa Cruz and UNC-Chapel Hill, respectively. When not building machine learning systems, Nathan spends as much time as possible with his favorite person in the world, his wife, as well as their four kids and two dogs, and enjoys getting outside to hike or garden and baking bread.
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// Related Links
Website: https://nrfrank.github.io/
Bisi: https://bisi.gitbook.io/bisi/
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Timestamps:
[00:00] Nathan's preferred coffee
[00:40] Takeaways
[02:00] Please leave a review in our comment sections! Please like, share, and subscribe to our MLOps channels!
[03:00] Telescope for gamma-ray burst
[07:31] Transition into ML
[11:23] Stats-heavy US sports commentary
[14:25] Building machine learning systems approach
[20:02] ML Workflow Must-Haves
[26:50] Love for tests
[33:10] Test Writing Importance
[34:37] Bridging Stakeholder Language Gap
[43:04] Shared Language, Team Collaboration
[47:28] Rapid fire questions
[51:20] Wrap up
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