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Exploring Machine Learning, AI, and Data Science

The Collision of AI, Data, and DevOps: Insights from KubeCon 2026

Welcome to Data Driven, where this week’s episode comes to you live from KubeCon 2026. The conversation focused on the emerging intersection of artificial intelligence, data, and DevOps, highlighting how Kubernetes is poised to play a pivotal role in advancing AI innovation. A key theme that emerged was the rise of tokenomics and the shifting economics of AI, particularly as organizations balance cloud and on-premise solutions in pursuit of cost control and privacy. The discussion explored the growing need for collaboration between IT admins, AI builders, and data scientists, and introduced new strategies for managing AI infrastructure in a rapidly evolving hybrid world. Tune in for insights into the future of AI deployment—and what it means for your career.

Transcript
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Hello and welcome back to Data Driven. This week, Frank is at KubeCon

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2026, and he is noticing a pattern.

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Kubernetes is about to make all of our artificial intelligence dreams

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come true. Now here's Frank.

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Hello, it's Frank here from

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Data Driven. And you

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don't know who I am. My name is Frank Lavinia, despite what the badge says.

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They got rid of the law. I'm here in the expo center

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before they start up. They're still setting up today. I don't think the

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expo center opens up for another hour or 2, but I figured I would

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do a quick livestream and I'm going to double down on my claim I made

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yesterday. Let me get my headphones off. I'm going to double down on my claim

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yesterday about how the AI world, the data world, saw how

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that has collided. I think the next logical step

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in the evolution of this is the collision of

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the AI world and the data

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world now colliding with DevOps. And from what I can tell,

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data operations, DataOps is going to collide slightly differently. I think you're going to

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have the monster that is data and AI or AI-related data

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implementations colliding with DevOps separately than from good

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old-fashioned DBA-type operations. The cool thing is

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that conferences tend to be a little bit ahead of the curve. So if you

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are wondering, like, where should I go next in my career or what I should

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do next? I would definitely investigate that space because regardless of

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what happens to one particular vendor because of a scandal,

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you're going to see AI is not walking back. AI is not going to slow

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down. AI, I think, is actually going to increase its footprint

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on-premise because the token

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prices, right? And that was the other theme that you see is control your token

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costs. A few years ago, you said token, people would have thought you meant

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Chuck E. Cheese or some kind of Bitcoin thing. But tokenomics now

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is a rising term and point of concern for many CFOs,

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definitely CFOs, probably also CEOs, as

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well as chief IT officers. So I think we're looking at

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a new world of AI where that is going to be a

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hybrid world first. And I say this out of

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personal experience, right? When I was at Microsoft, I was doing AI at Microsoft.

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I was all in on Microsoft, all in on the cloud. And then I realized,

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wait a minute, there's going to be some scenarios where I may not want certain

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types of data to leave my house, right?

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So then I set up my own home lab. I have the DGX Spark.

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And actually, fun fact, I actually now have a Qlik instance

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running in my home lab. And I tell you, it is, it has revolutionized

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how I use AI. And I'm not just saying that because I'm wearing a Qlik

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t-shirt. I'm telling you this as a home lab operator. And one of the reasons

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I like having a home lab It's not because it is fun, but it is

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also another home maintenance chore, right? But it lets me

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LARP, live action role play, as a CIO

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or a CTO, because that's de facto

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what I am at my house. Because I have a home lab, I have my

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family relying, the kids are relying on some of these services. My wife is the

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CISO because she works in information security. So she always

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lets me know when I do something wrong. But ultimately, I

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think one of the things that I've seen as a home

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labber is the impact of cost. If you're paying by

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tokens, the whole subscription thing where you pay $20 a

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month and you get unlimited or effectively unlimited,

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those days I think are numbered because when Anthropic IPOs,

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Wall Street is going to take a very different lens on a

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publicly traded company than a Privately funded startup.

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So I don't know that's going to happen days or weeks after the

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IPO, but it's going to happen within a set period of time

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after the IPO. So we're living in the golden age of

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cheap tokens because they're subsidized. That's coming to an end.

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And when that comes to an end, that $20 a month subscription could cost up

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to $200, right? And an economy like this where people are

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very anxious about AI taking over their jobs, Who is really going to pay

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that for their own personal use? I don't know. But I can

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tell you this, that the

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reason I joined Click was I saw that they had a vision and I saw

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that they had a vision to manage AI infrastructure, whether it's in the cloud,

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whether it's in on-prem, whether it's

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air-gapped, whatever. You as the administrator of the network, and I'm not,

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I'm not an IT admin, right? I haven't done anything remotely resembling IT

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specialized into development, client development, mobile development,

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and then AI development. I know that,

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I know what it is like some, some, some principles never change, but the core

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thing here that I want to point out is these people are going to have

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to work together, right? Admins, AI builders, AI data scientists, data

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engineers, that sort of thing, as well as people who are consuming

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AI, whether they're end-level consumers or developer-level consumers. When I say

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end user consumers, people that just click on their phone or their app and then

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they use AI. When I say developer consumers, the people that get an

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endpoint and they can just consume, right? They hit the endpoint and they do what

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it is. What really matters and what Qlik makes really easy

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is the ability for administrators to take control

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over their IT infrastructure through AI.

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Because you can route based on content, based on query, based on

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certain metadata about a request or just based on the size of the request,

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all these sorts of things you have control. You can route it to a public

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API. You can route it to a server running locally. I think we're

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seeing the golden age of this is about to— one golden age is

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going to end and a new one is going to start. And I think we're

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entering the golden age of on-prem IT and self-hosted AI.

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Just regardless of what Sam Altman gets mad about, people using RAM

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for local AI use cases. But those days, I really think you're going to

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see kind of these orchestrated systems where low

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memory consumption AI models are going to make

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decisions automatically for agents as to where they

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get their intelligence from. So that's all I got. I'll walk you around real briefly

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around the floor as we have. I'm going to do my extra

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special tool here. We're at the Salt Palace, I think it's

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called. Here, and we have the IBM booth, Everpure.

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The Qlik booth is just over there. I'll be over there today,

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booth number 276, I believe. So stop by and say hello.

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In the meantime, I'm signing off.

About the author, Frank

Frank La Vigne is a software engineer and UX geek who saw the light about Data Science at an internal Microsoft Data Science Summit in 2016. Now, he wants to share his passion for the Data Arts with the world.

He blogs regularly at FranksWorld.com and has a YouTube channel called Frank's World TV. (www.FranksWorld.TV). Frank has extensive experience in web and application development. He is also an expert in mobile and tablet engineering. You can find him on Twitter at @tableteer.