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