Explore the challenges of ML operations in a rapidly expanding company through this insightful conference talk from MLOps World: Machine Learning in Production. Learn how Udemy tackled multi-faceted growth challenges in ML platform and tooling, including developing a scalable platform for training and executing various ML models in real-time or batch. Discover how they built generic components to increase reuse, leading to faster delivery and lower maintenance costs. Understand their approach to efficiently serving different organizational needs with varying requirements, including unifying frameworks for both distributed and non-distributed applications. Gain insights into the increased focus on developer and data science ergonomics as the organization grew. Hear from Gulsen Kutluoglu, Director of Engineering, and Sam Cohan, Principal ML Engineer at Udemy, as they share best practices developed and discuss outstanding problems in their current state. This 56-minute talk provides valuable lessons for ML practitioners dealing with rapid growth and scaling challenges in their organizations.
Challenges for ML Operations in a Fast Growing Company
MLOps World: Machine Learning in Production via YouTube
Overview
Syllabus
Challenges for ML Operations in a Fast Growing Company
Taught by
MLOps World: Machine Learning in Production
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