AI Reliability, Spark, Observability, SLAs and Starting an AI Infra Company
LLMs are reshaping the future of data and AI—and ignoring them might just be career malpractice. Yoni Michael and Kostas Pardalis unpack what’s breaking, what’s emerging, and why inference is becoming the new heartbeat of the data pipeline.// BioKostas PardalisKostas is an engineer-turned-entrepreneur with a passion for building products and companies in the data space. He’s currently the co-founder of Typedef. Before that, he worked closely with the creators of Trino at Starburst Data on some exciting projects. Earlier in his career, he was part of the leadership team at Rudderstack, helping the company grow from zero to a successful Series B in under two years. He also founded Blendo in 2014, one of the first cloud-based ELT solutions.Yoni MichaelYoni is the Co-Founder of typedef, a serverless data platform purpose-built to help teams process unstructured text and run LLM inference pipelines at scale. With a deep background in data infrastructure, Yoni has spent over a decade building systems at the intersection of data and AI — including leading infrastructure at Tecton and engineering teams at Salesforce.Yoni is passionate about rethinking how teams extract insight from massive troves of text, transcripts, and documents — and believes the future of analytics depends on bridging traditional data pipelines with modern AI workflows. At Typedef, he’s working to make that future accessible to every team, without the complexity of managing infrastructure.// Related LinksWebsite: https://www.typedef.aihttps://techontherocks.showhttps://www.cpard.xyz~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreMLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Kostas on LinkedIn: /kostaspardalis/Connect with Yoni on LinkedIn: /yonimichael/Timestamps:[00:00] Breaking Tools, Evolving Data Workloads[06:35] Building Truly Great Data Teams[10:49] Making Data Platforms Actually Useful[18:54] Scaling AI with Native Integration[24:04] Empowering Employees to Build Agents[28:17] Rise of the AI Sherpa[36:09] Real AI Infrastructure Pain Points[38:05] Fixing Gaps Between Data, AI[46:04] Smarter Decisions Through Better Data[50:18] LLMs as Human-Machine Interfaces[53:40] Why Summarization Still Falls Short[01:01:15] Smarter Chunking, Fixing Text Issues[01:09:08] Evaluating AI with Canary Pipelines[01:11:46] Finding Use Cases That Matter[01:17:38] Cutting Costs, Keeping AI Quality[01:25:15] Aligning MLOps to Business Outcomes[01:29:44] Communities Thrive on Cross-Pollination[01:34:56] Evaluation Tools Quietly Consolidating
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