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Research#AI Infrastructure📝 BlogAnalyzed: Dec 29, 2025 07:42

Feature Platforms for Data-Centric AI with Mike Del Balso - #577

Published:Jun 6, 2022 19:28
1 min read
Practical AI

Analysis

This article summarizes a podcast episode from Practical AI featuring Mike Del Balso, CEO of Tecton. The discussion centers on feature platforms, previously known as feature stores, and their role in data-centric AI. The conversation covers the evolution of data infrastructure, the maturation of streaming data platforms, and the challenges of ML tooling, including the 'wide vs deep' paradox. The episode also explores the 'ML Flywheel' strategy and the construction of internal ML teams. The focus is on practical aspects of building and managing ML platforms.
Reference

“We explore the current complexity of data infrastructure broadly and how that has changed over the last five years, as well as the maturation of streaming data platforms.”

PermalinkPractical AI
Technology#Machine Learning📝 BlogAnalyzed: Dec 29, 2025 07:58

Feature Stores for MLOps with Mike del Balso - #420

Published:Oct 19, 2020 15:02
1 min read
Practical AI

Analysis

This article is a summary of a podcast episode from "Practical AI" featuring Mike del Balso, CEO of Tecton. The discussion centers around feature stores in the context of MLOps. The article highlights del Balso's experience building Uber's ML platform, Michelangelo, and his current work at Tecton. It covers the rationale behind focusing on feature stores, the challenges of operationalizing machine learning, and the capabilities mature platforms require. The conversation also touches on the differences between standalone components and feature stores, the use of existing databases, and the characteristics of a dynamic feature store. Finally, it explores Tecton's competitive advantages.
Reference

“In our conversation, Mike walks us through why he chose to focus on the feature store aspects of the machine learning platform...”

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Technology#Machine Learning Platforms📝 BlogAnalyzed: Dec 29, 2025 08:29

Machine Learning Platforms at Uber with Mike Del Balso - TWiML Talk #115

Published:Mar 1, 2018 19:01
1 min read
Practical AI

Analysis

This podcast episode from Practical AI features an interview with Mike Del Balso, Product Manager for Machine Learning Platforms at Uber. The discussion centers on the challenges and best practices for implementing machine learning within organizations. Del Balso highlights common pitfalls such as inadequate infrastructure for maintenance and monitoring, unrealistic expectations, and the lack of appropriate tools for data science and development teams. The interview also touches upon Uber's internal machine learning platform, Michelangelo, and the open-source distributed TensorFlow system, Horovod. The episode concludes with a call to action for listeners to vote in the #MyAI Contest.
Reference

“Mike shares some great advice for organizations looking to get value out of machine learning.”

PermalinkPractical AI