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"We have a perceptual AI, now what?" At Arc XP, we faced a daunting challenge with our video infrastructure: we trained a machine learning model to pick a thumbnail for a video, but due to the nature of its construction, deploying the model at scale proved difficult. Using the model against one or two videos a day was no problem, but what about thousands?

Orchestrating the machinery that makes the AI work at scale: The entire perceptual pipeline has many moving parts, including mechanisms for scene detection, key frame extraction, AWS resource manipulation, and, of course, the machine learning model itself. We used Temporal to bring order and sanity to this process, boiling the entire process down to a single, digestible, durable, and repeatable workflow.

"We have a perceptual AI, now what?" At Arc XP, we faced a daunting challenge with our video infrastructure: we trained a machine learning model to pick a thumbnail for a video

Working closely with the Temporal team themselves (shoutout to Chad Retz!), we identified areas for improvement and “correctness” in what should (and shouldn’t) be an activity and how “durable” each part should be.

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Video nativo. "We have a perceptual AI, now what?" At Arc XP, we faced a daunting challenge with our video infrastructure: we trained a machine learning model to pick a thumbnail for a video, but due to the nature of its construction, deploying the model at scale proved difficult. Using the model against one or two videos a day was no problem, but what about thousands?

Orchestrating the machinery that makes the AI work at scale: The entire perceptual pipeline has many moving parts, including mechanisms for scene detection, key frame extraction, AWS resource manipulation, and, of course, the machine learning model itself. We used Temporal to bring order and sanity to this process, boiling the entire process down to a single, digestible, durable, and repeatable workflow.

Working closely with the Temporal team themselves (shoutout to Chad Retz!), we identified areas for improvement and “correctness” in what should (and shouldn’t) be an activity and how “durable” each part should be.

"We have a perceptual AI, now what?" At Arc XP, we faced a daunting challenge with our video infrastructure: we trained a machine learning model to pick a thumbnail for a video, but due to the nature of its construction, deploying the model at scale proved difficult. Using the model against one or two videos a day was no problem, but what about thousands?

Orchestrating the machinery that makes the AI work at scale: The entire perceptual pipeline has many moving parts, including mechanisms for scene detection, key frame extraction, AWS resource manipulation, and, of course, the machine learning model itself. We used Temporal to bring order and sanity to this process, boiling the entire process down to a single, digestible, durable, and repeatable workflow.

Orchestrating the machinery that makes the AI work at scale: The entire perceptual pipeline has many moving parts, including mechanisms for scene detection, key frame extraction, AWS resource manipulation, and, of course, the machine learning model itself. We used Temporal to bring order and sanity to this process, boiling the entire process down to a single, digestible, durable, and repeatable workflow.

Working closely with the Temporal team themselves (shoutout to Chad Retz!), we identified areas for improvement and “correctness” in what should (and shouldn’t) be an activity and how “durable” each part should be.

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Orchestrating the machinery that makes the AI work at scale: The entire perceptual pipeline has many moving parts, including mechanisms for scene detection, key frame extraction, AWS resource manipulation, and, of course, the machine learning model itself. We used Temporal to bring order and sanity to this process, boiling the entire process down to a single, digestible, durable, and repeatable workflow.

"We have a perceptual AI, now what?" At Arc XP, we faced a daunting challenge with our video infrastructure: we trained a machine learning model to pick a thumbnail for a video, but due to the nature of its construction, deploying the model at scale proved difficult. Using the model against one or two videos a day was no problem, but what about thousands?

Orchestrating the machinery that makes the AI work at scale: The entire perceptual pipeline has many moving parts, including mechanisms for scene detection, key frame extraction, AWS resource manipulation, and, of course, the machine learning model itself. We used Temporal to bring order and sanity to this process, boiling the entire process down to a single, digestible, durable, and repeatable workflow.

Orchestrating the machinery that makes the AI work at scale: The entire perceptual pipeline has many moving parts, including mechanisms for scene detection, key frame extraction, AWS resource manipulation, and, of course, the machine learning model itself. We used Temporal to bring order and sanity to this process, boiling the entire process down to a single, digestible, durable, and repeatable workflow.

Orchestrating the machinery that makes the AI work at scale: The entire perceptual pipeline has many moving parts, including mechanisms for scene detection, key frame extraction, AWS resource manipulation, and, of course, the machine learning model itself. We used Temporal to bring order and sanity to this process, boiling the entire process down to a single, digestible, durable, and repeatable workflow.

Working closely with the Temporal team themselves (shoutout to Chad Retz!), we identified areas for improvement and “correctness” in what should (and shouldn’t) be an activity and how “durable” each part should be.

Botón IG específico

Orchestrating the machinery that makes the AI work at scale: The entire perceptual pipeline has many moving parts, including mechanisms for scene detection, key frame extraction, AWS resource manipulation, and, of course, the machine learning model itself. We used Temporal to bring order and sanity to this process, boiling the entire process down to a single, digestible, durable, and repeatable workflow.

"We have a perceptual AI, now what?" At Arc XP, we faced a daunting challenge with our video infrastructure: we trained a machine learning model to pick a thumbnail for a video, but due to the nature of its construction, deploying the model at scale proved difficult. Using the model against one or two videos a day was no problem, but what about thousands?

Orchestrating the machinery that makes the AI work at scale: The entire perceptual pipeline has many moving parts, including mechanisms for scene detection, key frame extraction, AWS resource manipulation, and, of course, the machine learning model itself. We used Temporal to bring order and sanity to this process, boiling the entire process down to a single, digestible, durable, and repeatable workflow.

Orchestrating the machinery that makes the AI work at scale: The entire perceptual pipeline has many moving parts, including mechanisms for scene detection, key frame extraction, AWS resource manipulation, and, of course, the machine learning model itself. We used Temporal to bring order and sanity to this process, boiling the entire process down to a single, digestible, durable, and repeatable workflow.

Orchestrating the machinery that makes the AI work at scale: The entire perceptual pipeline has many moving parts, including mechanisms for scene detection, key frame extraction, AWS resource manipulation, and, of course, the machine learning model itself. We used Temporal to bring order and sanity to this process, boiling the entire process down to a single, digestible, durable, and repeatable workflow.

Working closely with the Temporal team themselves (shoutout to Chad Retz!), we identified areas for improvement and “correctness” in what should (and shouldn’t) be an activity and how “durable” each part should be.

Código embeber IG

"We have a perceptual AI, now what?" At Arc XP, we faced a daunting challenge with our video infrastructure: we trained a machine learning model to pick a thumbnail for a video, but due to the nature of its construction, deploying the model at scale proved difficult. Using the model against one or two videos a day was no problem, but what about thousands?

Orchestrating the machinery that makes the AI work at scale: The entire perceptual pipeline has many moving parts, including mechanisms for scene detection, key frame extraction, AWS resource manipulation, and, of course, the machine learning model itself. We used Temporal to bring order and sanity to this process, boiling the entire process down to a single, digestible, durable, and repeatable workflow.

Orchestrating the machinery that makes the AI work at scale: The entire perceptual pipeline has many moving parts, including mechanisms for scene detection, key frame extraction, AWS resource manipulation, and, of course, the machine learning model itself. We used Temporal to bring order and sanity to this process, boiling the entire process down to a single, digestible, durable, and repeatable workflow.

Working closely with the Temporal team themselves (shoutout to Chad Retz!), we identified areas for improvement and “correctness” in what should (and shouldn’t) be an activity and how “durable” each part should be.

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Orchestrating the machinery that makes the AI work at scale: The entire perceptual pipeline has many moving parts, including mechanisms for scene detection, key frame extraction, AWS resource manipulation, and, of course, the machine learning model itself. We used Temporal to bring order and sanity to this process, boiling the entire process down to a single, digestible, durable, and repeatable workflow.

"We have a perceptual AI, now what?" At Arc XP, we faced a daunting challenge with our video infrastructure: we trained a machine learning model to pick a thumbnail for a video, but due to the nature of its construction, deploying the model at scale proved difficult. Using the model against one or two videos a day was no problem, but what about thousands?

Orchestrating the machinery that makes the AI work at scale: The entire perceptual pipeline has many moving parts, including mechanisms for scene detection, key frame extraction, AWS resource manipulation, and, of course, the machine learning model itself. We used Temporal to bring order and sanity to this process, boiling the entire process down to a single, digestible, durable, and repeatable workflow.

Working closely with the Temporal team themselves (shoutout to Chad Retz!), we identified areas for improvement and “correctness” in what should (and shouldn’t) be an activity and how “durable” each part should be.

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"We have a perceptual AI, now what?" At Arc XP, we faced a daunting challenge with our video infrastructure: we trained a machine learning model to pick a thumbnail for a video, but due to the nature of its construction, deploying the model at scale proved difficult. Using the model against one or two videos a day was no problem, but what about thousands?

Working closely with the Temporal team themselves (shoutout to Chad Retz!), we identified areas for improvement and “correctness” in what should (and shouldn’t) be an activity and how “durable” each part should be.

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"We have a perceptual AI, now what?" At Arc XP, we faced a daunting challenge with our video infrastructure: we trained a machine learning model to pick a thumbnail for a video, but due to the nature of its construction, deploying the model at scale proved difficult. Using the model against one or two videos a day was no problem, but what about thousands?

Orchestrating the machinery that makes the AI work at scale: The entire perceptual pipeline has many moving parts, including mechanisms for scene detection, key frame extraction, AWS resource manipulation, and, of course, the machine learning model itself. We used Temporal to bring order and sanity to this process, boiling the entire process down to a single, digestible, durable, and repeatable workflow.

Working closely with the Temporal team themselves (shoutout to Chad Retz!), we identified areas for improvement and “correctness” in what should (and shouldn’t) be an activity and how “durable” each part should be.

"We have a perceptual AI, now what?" At Arc XP, we faced a daunting challenge with our video infrastructure: we trained a machine learning model to pick a thumbnail for a video, but due to the nature of its construction, deploying the model at scale proved difficult. Using the model against one or two videos a day was no problem, but what about thousands?

Orchestrating the machinery that makes the AI work at scale: The entire perceptual pipeline has many moving parts, including mechanisms for scene detection, key frame extraction, AWS resource manipulation, and, of course, the machine learning model itself. We used Temporal to bring order and sanity to this process, boiling the entire process down to a single, digestible, durable, and repeatable workflow.

Working closely with the Temporal team themselves (shoutout to Chad Retz!), we identified areas for improvement and “correctness” in what should (and shouldn’t) be an activity and how “durable” each part should be.

"We have a perceptual AI, now what?" At Arc XP, we faced a daunting challenge with our video infrastructure: we trained a machine learning model to pick a thumbnail for a video, but due to the nature of its construction, deploying the model at scale proved difficult. Using the model against one or two videos a day was no problem, but what about thousands?

Orchestrating the machinery that makes the AI work at scale: The entire perceptual pipeline has many moving parts, including mechanisms for scene detection, key frame extraction, AWS resource manipulation, and, of course, the machine learning model itself. We used Temporal to bring order and sanity to this process, boiling the entire process down to a single, digestible, durable, and repeatable workflow.

Working closely with the Temporal team themselves (shoutout to Chad Retz!), we identified areas for improvement and “correctness” in what should (and shouldn’t) be an activity and how “durable” each part should be.

"We have a perceptual AI, now what?" At Arc XP, we faced a daunting challenge with our video infrastructure: we trained a machine learning model to pick a thumbnail for a video, but due to the nature of its construction, deploying the model at scale proved difficult. Using the model against one or two videos a day was no problem, but what about thousands?

Orchestrating the machinery that makes the AI work at scale: The entire perceptual pipeline has many moving parts, including mechanisms for scene detection, key frame extraction, AWS resource manipulation, and, of course, the machine learning model itself. We used Temporal to bring order and sanity to this process, boiling the entire process down to a single, digestible, durable, and repeatable workflow.

Working closely with the Temporal team themselves (shoutout to Chad Retz!), we identified areas for improvement and “correctness” in what should (and shouldn’t) be an activity and how “durable” each part should be.

"We have a perceptual AI, now what?" At Arc XP, we faced a daunting challenge with our video infrastructure: we trained a machine learning model to pick a thumbnail for a video, but due to the nature of its construction, deploying the model at scale proved difficult. Using the model against one or two videos a day was no problem, but what about thousands?

Orchestrating the machinery that makes the AI work at scale: The entire perceptual pipeline has many moving parts, including mechanisms for scene detection, key frame extraction, AWS resource manipulation, and, of course, the machine learning model itself. We used Temporal to bring order and sanity to this process, boiling the entire process down to a single, digestible, durable, and repeatable workflow.

Working closely with the Temporal team themselves (shoutout to Chad Retz!), we identified areas for improvement and “correctness” in what should (and shouldn’t) be an activity and how “durable” each part should be.

"We have a perceptual AI, now what?" At Arc XP, we faced a daunting challenge with our video infrastructure: we trained a machine learning model to pick a thumbnail for a video, but due to the nature of its construction, deploying the model at scale proved difficult. Using the model against one or two videos a day was no problem, but what about thousands?

Orchestrating the machinery that makes the AI work at scale: The entire perceptual pipeline has many moving parts, including mechanisms for scene detection, key frame extraction, AWS resource manipulation, and, of course, the machine learning model itself. We used Temporal to bring order and sanity to this process, boiling the entire process down to a single, digestible, durable, and repeatable workflow.

Working closely with the Temporal team themselves (shoutout to Chad Retz!), we identified areas for improvement and “correctness” in what should (and shouldn’t) be an activity and how “durable” each part should be.

"We have a perceptual AI, now what?" At Arc XP, we faced a daunting challenge with our video infrastructure: we trained a machine learning model to pick a thumbnail for a video, but due to the nature of its construction, deploying the model at scale proved difficult. Using the model against one or two videos a day was no problem, but what about thousands?

Orchestrating the machinery that makes the AI work at scale: The entire perceptual pipeline has many moving parts, including mechanisms for scene detection, key frame extraction, AWS resource manipulation, and, of course, the machine learning model itself. We used Temporal to bring order and sanity to this process, boiling the entire process down to a single, digestible, durable, and repeatable workflow.

Working closely with the Temporal team themselves (shoutout to Chad Retz!), we identified areas for improvement and “correctness” in what should (and shouldn’t) be an activity and how “durable” each part should be.