When models go rogue: Hard earned lessons on using machine learning in production
A lot of progress has been made over the past decade on process & tooling managing large-scale, multi-tier, multi-cloud apps & API's. In contrast, there is far less common knowledge on best practices for managing machine learned models (classifiers, forecasters, etc.) - especially beyond the modeling / optimization / deployment process, once they are in production.
This talk summarizes best practices & lessons learned across the entire life cycle of such systems, based on nearly a decade of experience building & operating such systems at Fortune 500 companies across several industries. The talk is intended for engineering leaders, architects, data science & DevOps leaders, and covers aspects of the development process, measurement, accuracy, feedback, operations and project management.
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