Building the data stack for operational ML

apply(meetup) - Feb '22 - 20 minutes

Mike will kickoff the event and present his views on building the optimal data stack for Operational ML.

Operational ML consists in deploying models in production to power new applications and use cases, including fraud detection, product recommendations, search, risk evaluation, and pricing. Operational ML models often benefit from using not only batch features, but also streaming and real-time features, to make predictions on the freshest data available. Unfortunately, building features for operational ML is hard. It requires developing bespoke pipelines to transform batch and real-time data, generating accurate training datasets, and serving data online at high volume and low-latency.

Mike will present his views on the ideal data stack for operational ML. By combining feature stores, scalable offline storage, elastic processing, and high-performance online storage, organizations can lay an optimal infrastructure foundation for their ML data. This stack allows data engineers to build real-time features quickly and reliably, and deploy them to production with enterprise-grade SLAs. Mike will discuss the latest trends and his vision for the future.


Mike Del Balso

Co-Founder & CEO

Tecton

Mike Del Balso is the co-founder of Tecton, where he is focused on building next-generation data infrastructure for Operational ML. Before Tecton, Mike was the PM lead for the Uber Michelangelo ML platform. He was also a product manager at Google where he managed the core ML systems that power Google’s Search Ads business. Previous to that, he worked on Google Maps. He holds a BSc in Electrical and Computer Engineering summa cum laude from the University of Toronto.