Video: How Video AI Helps Businesses Interpret Experience Metrics
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Read the complete transcript of this clip:
Josh Gray: We deal kind of underneath the actual video delivery itself, and deal with more the experience metrics of how the video's getting there, how the playback is occurring. It's a similar kind of pattern, and I wanted to underscore that a lot of the good use cases for AI that we've seen are where you have to meaningfully interpret metrics that are fuzzy and noisy in nature.
The classic examples are things like OCR handwriting--if you think about that, everybody's handwriting is a little bit different, but virtually any place where you're taking large volumes of metrics out of the real world, and doing something meaningful with them, there is incredible noise and variation. And that's a great application of where you can train systems to extract the meaningful patterns that underlie the noise and variation.
In our case we do a lot with playback metrics, delivery metrics, and performance metrics all around the world, to be able to get the clean signals that tell us how we should change the systems to meaningfully improve video content delivery.
Video quality measurement is more crucial than ever. In this buyers' guide, we'll identify the most commonly used metrics and discuss tools that deploy them.
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