Ah, yeah, that one liner resonates with teams establishing their vision infra. Teams build production vision models in the span of an afternoon with better tools, e.g. one dev built this Mountain Dew bottle model during Super Bowl half time.

Some ML engineers find value in things like automated annotation, testing model architectures (models.roboflow.com), and having one-click deploy for custom object detection APIs. Think of it like replacing all the one-off scripts so you can focus on your domain-specific problems instead of reinventing the wheel on vision infrastructure.

Or maybe they don't care to make a pitch as in sell you something, but just wanted to briefly explain it to that other dude?