Practical Approach to ML and AI System Design // Nathan Ryan Frank // MLOps Podcast #199 clip
MLOps podcast #199 with Nathan Ryan Frank, Director, Machine Learning Platform & Operations at WW Grainger, Challenges Operationalizing Machine Learning (And Some Solutions).
Nathan Ryan Frank, the director of machine learning platform and operations at Grainger, shares his insights on designing systems for machine learning and AI products. He emphasizes the importance of understanding the pain points in the process and choosing between utilizing existing tools or developing internal solutions. Frank also delves into the significance of maintaining standards and structures within teams, allowing for ease of onboarding and adaptation.
// 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
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