Speed and Scale: How Today's AI Datacenters Are Operating Through Hypergrowth

Speed and Scale: How Today's AI Datacenters Are Operating Through Hypergrowth

Author: Demetrios February 3, 2026 Duration: 1:07:16
MLOps.community
Technology

Kris Beevers is the CEO at NetBox Labs, working on turning NetBox into the system of record and automation backbone for modern and AI-driven infrastructure.


Speed and Scale: How Today's AI Datacenters Are Operating Through Hypergrowth // MLOps Podcast #359 with Kris Beevers, CEO of NetBox Labs


Join the Community: https://go.mlops.community/YTJoinIn

Get the newsletter: https://go.mlops.community/YTNewsletter

MLOps GPU Guide: https://go.mlops.community/gpuguide


// Abstract

Hundreds of neocloud operators and "AI Factory" builders have emerged to serve the insatiable demand for AI infrastructure. These teams are compressing the design, build, deploy, operate, scale cycle of their infrastructures down to months, while managing massive footprints with lean teams. How? By applying modern intent-driven infrastructure automation principles to greenfield deployments. We'll explore how these teams carry design intent through to production, and how operating and automating around consistent infrastructure data is compressing "time to first train".


// Bio

Kris Beevers is the Co-founder and CEO of NetBox Labs. NetBox is used by nearly every Neocloud and AI datacenter to manage their networks and infrastructure. Kris is an engineer at heart and by background, and loves the leverage infrastructure innovation creates to accelerate technology and empower engineers to do their best work. A serial entrepreneur, Kris has founded and helped lead multiple other successful businesses in the internet and network infrastructure. Most recently, he co-founded and led NS1, which was acquired by IBM in 2023. He holds a Ph.D. in Computer Science from Rensselaer Polytechnic Institute and is based in New Jersey.


// Related Links

Website: https://netboxlabs.com/

Coding Agents Conference: https://luma.com/codingagents


~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~

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

Join our Slack community [https://go.mlops.community/slack]

Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)]

Sign up for the next meetup: [https://go.mlops.community/register]

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


Connect with Demetrios on LinkedIn: /dpbrinkm

Connect with Kris on LinkedIn: /beevek/


Timestamps:

[00:00] Observability and Delta Analysis

[00:26] New World Exploration

[04:06] Bottlenecks in AI Infrastructure

[13:37] Data Center Optimization Challenges

[19:58] Tech Stack Breakdown

[25:26] Data Center Design Principles

[31:32] Constraints and Automation in Design

[40:00] Complexity in Data Centers

[45:02] GPU Cloud Landscape

[50:24] Data Centers in Containers

[57:45] Observability Beyond Software

[1:04:43] Tighter Integrations vs NetBox

[1:06:47] Wrap up


Hosted by Demetrios, MLOps.community is a space for honest, meandering talks about the real work of making artificial intelligence systems actually work. This isn't about hype or theoretical papers; it's about the messy, practical, and often surprising journey of taking models from a notebook into a live environment. You'll hear from engineers and practitioners who are in the trenches, discussing the tools, the frustrations, and the occasional breakthroughs that define the day-to-day. The conversations are deliberately relaxed, covering everything from traditional machine learning pipelines to the new world of large language models and even the intangible "vibes" of team culture and process. Each episode peels back a layer on what "production" really means, whether that involves deploying a predictive service, managing an agentic system, or maintaining reliability as everything scales. Tuning into this podcast feels like grabbing a coffee with colleagues who aren't afraid to dig into the technical nitty-gritty while keeping the tone conversational and accessible. It's for anyone who builds, manages, or is just curious about the operational backbone that allows AI to deliver value, offering a grounded perspective often missing from the broader conversation.
Author: Language: en-us Episodes: 100

Official website RSS
MLOps.community
Podcast Episodes
Voice Agent Use Cases [not-audio_url] [/not-audio_url]

Duration: 51:04
This episode is brought to you by the MLflow team. Check out more information at MLflow.org.What does it actually take to build voice AI at a billion-interaction scale? This episode features an ex-Amazon voice AI enginee…
It's 2026, and We're Still Talking Evals [not-audio_url] [/not-audio_url]

Duration: 40:56
Maggie Konstanty is an AI Product Manager at Prosus, one of the world's largest consumer internet companies, where she builds and evaluates AI agents for food ordering and ecommerce at scale. She's been inside the messy…
Why Agents are Driving Software Development to the Cloud [not-audio_url] [/not-audio_url]

Duration: 51:07
This episode is brought to you by Hyperbolic and the MLflow team. Check out more information at hyperbolic.ai and MLflow.org.Why AI Coding Agents Are Moving to the Cloud — With Zach Lloyd, CEO of WarpZach Lloyd is the fo…
The Modern Software Engineer [not-audio_url] [/not-audio_url]

Duration: 53:37
This episode is brought to you by the MLflow team. Check out more information at MLflow.org.Mihail Eric is Head of AI at Monaco and Adjunct Lecturer at Stanford University, where he teaches CS146S: "The Modern Software D…
We Cut LLM Latency by 70% in Production [not-audio_url] [/not-audio_url]

Duration: 1:05:20
Maher Hanafi is an engineering leader who went from zero AI experience to self-hosting LLMs at enterprise scale — managing GPU costs, optimizing inference with TensorRT LLM, and building an AI platform for HR tech. In th…
Getting Humans Out of the Way: How to Work with Teams of Agents [not-audio_url] [/not-audio_url]

Duration: 50:30
Rob Ennals is the creator of Broomy, an open-source IDE designed for working effectively with many agents in parallel. He previously worked at Meta, Quora, Google Search, and Intel Research. He has a PhD in Computer Scie…
Fixing GPU Starvation in Large-Scale Distributed Training [not-audio_url] [/not-audio_url]

Duration: 52:48
Kashish Mittal is a Staff Software Engineer at Uber, working on large-scale distributed systems and core backend infrastructure.Fixing GPU Starvation in Large-Scale Distributed Training // MLOps Podcast #367 with Kashish…

«123456...10»