Ben Lorica Explains the PARK Stack the emerging standard for Production AI

Описание: In this conversation, we sit down with Ben Lorica - Principal at Gradient Flow, host of The Data Exchange podcast, former Chief Data Scientist at O’Reilly Media, and co-chair of several leading industry conferences - to unpack the PARK stack and why it’s emerging as a common pattern for building production AI systems.

Ben coined the term PARK stack to describe an architecture increasingly used by teams building custom AI platforms at scale:
PyTorch for training and post-training workflows,
open models for customization and inference,
Ray for distributed compute,
and Kubernetes for production orchestration

We discuss how enterprises are approaching AI infrastructure today, the tradeoffs between API-only approaches versus custom platforms, and why distributed inference and heterogeneous compute are becoming central concerns in production AI. Ben also shares perspective on talent, hiring, and how open-source ecosystems lower the barrier for enterprises adopting modern AI infrastructure.

This conversation is especially relevant for AI platform teams, infrastructure engineers, and enterprise leaders thinking about how to move AI systems from experimentation into reliable, scalable production.

📄 Ben’s article on the PARK stack:
https://thedataexchange.media/the-par...

📬 Ben’s newsletter, Gradient Flow:
https://gradientflow.substack.com/

⏱️ Timestamps
00:00 Ben Lorica on AI Infrastructure and Production AI
00:40 Three Enterprise AI Infrastructure Choices
02:57 What Is the PARK Stack? (PyTorch, Models, Ray, Kubernetes)
04:01 PARK Stack Components and Why They Work Together
07:45 Production AI Systems: Inference, Hardware, and Efficiency
11:53 AI Infrastructure Talent, Hiring, and Open-Source Communities
13:48 Thoughts on the Future of Production AI

Host in the video: ‪@LindaVivah‬ , Staff Developer Advocate at Anyscale

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