The Future of Feature Stores and Platforms // Mike Del Balso & Josh Wills // # 186

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Oct 31, 2023

Mike Del Balso, CEO of Tecton, and Josh Wills, Angel Investor, discuss the importance of feature stores and platforms for ML applications. They explore challenges in operationalizing data pipelines, integrating offline and online data stores, and templatizing workflows for fraud detection and recommendation systems. The concept of gamification in feature creation is also explored.

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Introduction

00:00 • 2min

Feature Stores and Platforms: Exploring the Future and History

01:40 • 4min

Building Highly Productive ML Applications at Google

05:50 • 8min

The Importance of Operationalizing Data Pipelines

14:03 • 32min

Integrating offline and online data stores with a feature store

45:36 • 21min

Exploring the Potential of Templatizing Workflows in Feature Stores and Platforms

01:06:14 • 2min

Exploring the concept of gamification in feature creation

01:08:36 • 3min

MLOps podcast #186 with Mike Del Balso, CEO & Co-founder of Tecton and Josh Wills, Angel Investor, The Future of Feature Stores and Platforms. // Abstract Mike and Josh discuss creating templates and working at a detailed level, exploring Tecton's potential for sharing fraud and third-party features. They focus on technical aspects like data handling and optimizing models, emphasizing the significance of quality data for AI systems and the necessity for cohesive feature infrastructure in reaching production stages. // Bio Mike Del Balso Mike 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. Josh Wills Josh Wills is an angel investor specializing in data and machine learning infrastructure. He was formerly the head of data engineering at Slack, the director of data science at Cloudera, and a software engineer at Google. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links ⁠ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Mike on LinkedIn: https://www.linkedin.com/in/michaeldelbalso/Connect with Josh on LinkedIn: https://www.linkedin.com/in/josh-wills-13882b/ Timestamps: [00:00] Introduction to Mike [01:45] Takeaways [03:32] Features of the new paradigm of ML and LLMs [06:00] D. Sculley's papers [13:05] The birth of Feature Store [17:06] Data Pipeline Challenges Addressed [20:00] Operationalizing [26:50] Feature Store Challenges [30:26] Z access [36:23] Addressing Technical Debt Challenges [37:27] Real-Time vs. Batch Processing [47:10] Feature Store Evolution: Apache Iceberg [49:59] Feature Platform: Dedicated Query Engine [54:04] The bottleneck [56:00] LLMs, Feature Stores Overview [1:00:20] Vector databases [1:06:15] Workflow Templating Efficiency [1:08:35] Gamification suggestion for Tecton [1:10:25] Wrap up