Machine learning is opening a new chapter in video compression, with the potential to improve quality, reduce bandwidth, and enable better video experiences across diverse devices and network conditions. But taking ML-powered codecs from research to real-world products requires navigating tough tradeoffs in latency, compute cost, hardware acceleration, compatibility, and quality measurement.
This talk shares the product and engineering considerations behind developing and scaling ML Video Codec (MLVC) in Microsoft Teams, and what it takes to turn a promising model into durable product impact.
