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 John Snow Labs, helping healthcare & life science companies put AI to good use. David is the creator of Spark NLP – the world’s most widely used natural language processing library in the enterprise. He has extensive experience building and running web-scale software platforms and teams – in startups, for Microsoft’s Bing in the US and Europe, and to scale Amazon’s financial systems in Seattle and the UK. David holds a PhD in computer science and master’s degrees in both computer science and business administration.