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How to Monitor Machine Learning Stacks - Detecting and Addressing Special Issues

Offered By: Toronto Machine Learning Series (TMLS) via YouTube

Tags

Machine Learning Courses Data Science Courses Anomaly Detection Courses Performance Evaluation Courses Recommendation Systems Courses Model Deployment Courses

Course Description

Overview

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Discover effective strategies for monitoring machine learning stacks in this insightful conference talk by Lina Weichbrodt, Machine Learning Lead Engineer at DKB Bank. Learn why traditional monitoring methods often fail to detect critical issues in ML services and explore innovative solutions to address these challenges. Delve into practical examples that illustrate the unique problems faced by machine learning systems and gain knowledge about additional metrics that can be implemented to enhance detection capabilities. Examine a compelling case study from Zalando, one of Europe's leading fashion retailers, showcasing how these new metrics are applied to their recommendation stacks. Gain valuable insights into improving the reliability and performance of machine learning services beyond the conventional "four golden signals" of latency, errors, traffic, and saturation.

Syllabus

Lina Weichbrodt - How To Monitor Machine Learning Stacks


Taught by

Toronto Machine Learning Series (TMLS)

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