Speaker "David Talby" Details Back



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.


David Talby is a chief technology officer at Pacific AI, helping fast-growing companies apply big data and data science techniques to solve real-world problems in healthcare, life science, and related fields. David has extensive experience in building and operating web-scale data science and business platforms, as well as building world-class, Agile, distributed teams. Previously, he was with Microsoft’s Bing Group, where he led business operations for Bing Shopping in the US and Europe, and worked at Amazon both in Seattle and the UK, where he built and ran distributed teams that helped scale Amazon’s financial systems. David holds a PhD in computer science and master’s degrees in both computer science and business administration.