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Managing the Flywheel of ML Data

Posted Apr 12
# Data engineering
# Feature Stores
# Systems and Architecture
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SPEAKER
Mike Del Balso
Mike Del Balso
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 GoogleÕs 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.

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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 GoogleÕs 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.

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SUMMARY

The ML EngineerÕs life has become significantly easier over the past few years, but ML projects are still too tedious and complex. Feature stores have recently emerged as an important product category within the MLOps ecosystem. They solve part of the data problem for ML by automating feature processing and serving.

But feature stores are not enough. What data teams need is a platform that automates the complete lifecycle of ML features. This platform must provide integrations with the modern DevOps and data ecosystems, including the Modern Data Stack. It should provide excellent support for advanced use cases like Recommender Systems and Fraud. And it should automate the data feedback loop, abstracting away tasks like data logging and training dataset generation. In this talk, Mike will cover his vision for the evolution of the feature store into this complete feature platform for ML.

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