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MLOps.community
Demetrios
457 episodes
2 days ago
Relaxed Conversations around getting AI into production, whatever shape that may come in (agentic, traditional ML, LLMs, Vibes, etc)
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Technology
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All content for MLOps.community is the property of Demetrios and is served directly from their servers with no modification, redirects, or rehosting. The podcast is not affiliated with or endorsed by Podjoint in any way.
Relaxed Conversations around getting AI into production, whatever shape that may come in (agentic, traditional ML, LLMs, Vibes, etc)
Show more...
Technology
https://d3t3ozftmdmh3i.cloudfront.net/staging/podcast_uploaded_episode/3809022/3809022-1749214529082-9de556df4d6e4.jpg
Hard Learned Lessons from Over a Decade in AI
MLOps.community
48 minutes 42 seconds
1 week ago
Hard Learned Lessons from Over a Decade in AI

Tecton⁠ Founder and CEO Mike Del Balso talks about what ML/AI use cases are core components generating Millions in revenue. Demetrios and Mike go through the maturity curve that predictive Machine Learning use cases have gone through over the past 5 years, and why a feature store is a primary component of an ML stack.


// Bio


Mike Del Balso is the CEO and co-founder of Tecton, where he’s building the industry’s first feature platform for real-time ML. Before Tecton, Mike co-created 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. He studied Applied Science, Electrical & Computer Engineering at the University of Toronto.


// Related Links


Website: www.tecton.ai


~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~


Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore

MLOps Swag/Merch: [https://shop.mlops.community/]

Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Mike on LinkedIn: /michaeldelbalso


Timestamps:


[00:00] Smarter decisions, less manual work

[03:52] Data pipelines: pain and fixes

[08:45] Why Tecton was born

[11:30] ML use cases shift

[14:14] Models for big bets

[18:39] Build or buy drama

[20:20] Fintech's data playbook

[23:52] What really needs real-time

[28:07] Speeding up ML delivery

[32:09] Valuing ML is tricky

[35:29] Simplifying ML toolkits

[37:18] AI copilots in action

[42:13] AI that fights fraud

[45:07] Teaming up across coasts

[46:43] Tecton + Generative AI?

MLOps.community
Relaxed Conversations around getting AI into production, whatever shape that may come in (agentic, traditional ML, LLMs, Vibes, etc)