August 7, 2018
David Talby, Ph.D.
Much progress has been made over the past decade on process and tooling for managing large-scale, multitier, multicloud apps and APIs, but there is far less common knowledge on best practices for managing machine-learned models (classifiers, forecasters, etc.), especially once models are in production. Machine learning systems often fail in production in unexpected ways. This set of real-world case studies shows why this happens and explains what you can do about it, covering best practices and lessons learned from a decade of experience building and operating such systems at Fortune 500 companies. Topics include concept drift, selecting the right retrain pipeline, A/B testing challenges, offline vs. online measurement, adversarial learning systems, and their impact on project management.
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