Ex-Disney Tech Ops Expert Sarge Sargent Talks AI/ML and Building Better Dashboards
In this wide-ranging interview from Streaming Media 2025, streaming industry vets Sarge Sargent and Timothy Fore-Siglin talk leveraging and deploying machine learning (ML) and generative AI (Gen AI) beyond the hype.
Sargent’s Varied Career
Streaming Media contributing editor Fore-Siglin says, “Sarge, you’ve been around the industry for quite some time. For our audience members who may not know, give me a little bit of your background.”
Sargent says he started in new media at The Washington Post in the mid-1990s, when he helped launch the paper’s website. “Fast forward to 2007. I was already at Adobe, and they formulated the technical account management team for Flash media servers. … For a long time, I did [that], then Adobe brought Auditude in and formed a Primetime team. So I became an enablement engineer for Primetime and then went on to do management at Primetime. From Primetime, I went over to NBC Sports, who had been a customer, and I was on the launch team for Peacock. So I ran the live video workflows for Peacock. And then from there I went over to Disney to run a technical operations team for the ad platforms at Disney.” He was at Disney until very recently.
This list of workplaces reminds Fore-Siglin that they know many of the same people in the industry, and yet he’s never met Sargent before. “It’s so good to finally get a chance to sit down and talk,” he says. “So now you’re doing what? What’s your role?”
“I am just looking for my next role. What I want to do and what I want to focus on is the intersection of AI, sports, and video,” Sargent replies.
Fore-Siglin wonders, “Where would you sort of fit the best in the industry? Because the industry, as you say, is shifting toward AI. Where do you see the strength?”
Sargent says that no matter the industry shifts, he’s been able to pivot. “I come from a web development background. Shift that to video, doing a lot of support. But then when we brought Primetime in, shifting over to ads. So I’ve kind of seen it all, done it all as far as inserting ads into a live video stream for the highest-scaled events, and then running operations for that, including incident management.” He continues, “When we look at what’s happening with AI and the emergence in the industry, whether it be [Gen] AI or whether it’s just pure ML and looking at huge data models and using that data to make informed decisions about what’s going on in stream, that’s kind of where I thrive.”
Fore-Siglin appreciates “you saying ML because anytime I say to somebody ‘machine learning,’ they sort of look at me blankly and I’m like, ‘This is what you call AI. You can talk about the algorithms.’”
AI’s Impact on Gathering Good Data
Fore-Siglin points to a previous interview he did that focused on metadata’s importance for making decisions around ads. “What’s your take on where we’re going in this combination of better metadata, maybe more standardized metadata, and AI and machine learning? How do those play together?”
Sargent agrees, “Metadata is huge. I heard someone describe it as the new oil.” It’s necessary to be able to describe both content and viewers, he notes, “and then pair them together so you can get better personalization as far as content delivery, and then also better personalization with your ad delivery.”
AI should provide faster access to that data, Sargent believes. “One of the pain points we have from an operational standpoint is, when something goes wrong in the stream and we want to measure impact, you may have huge loads of data, but they may be in so many disparate systems. How do you quickly write a query to … first pull into the same data set, and then you can go iterate over it? So hopefully the new trend will be to use AI and ML to do those things in real time so that operations teams can make informed decisions.”
Fore-Siglin brings up the issue of standardization, which would allow the query to work across all of the different points. Sargent chimes in, “Yeah, so it’s reusable.” Fore-Siglin laments the large number of available dashboards that aren’t compatible with each other.
Sargent agrees, adding, “And they may not be correct. So they may be looking at the wrong data or at the wrong period of time for the data.” His solution is to leverage ML and Gen AI to create a more dynamic dashboard.
Fore-Siglin summarizes, “So in other words, you essentially do the algorithms beneath to massage it together and then use Gen AI to lay it out.”
Sargent says this is agentic AI: “You build your bot that will consistently mine this data for you so that at any moment of time, you can pull up a view—that’s generated on-the-fly—of that dataset so you can make some decisions on it.”
“That’s actually a really clever approach to it because you’re right, you’re using the benefits of each of those parts to do that,” Fore-Siglin says. “All right, well, that alone should get you your next gig.”
Sargent laughs. “I hope so.”
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