Meet the AI helping you choose what to watch next (Ep. 494)

We chat with the engineers behind some of the world's biggest streaming platforms.

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Today’s streaming services have a vast content library, and a vastly different programming challenge than traditional television stations. Where linear TV tries to schedule content into time slots so the right demographics will see it and enjoy it, streaming services let you watch any content, any time you want. That means these service need to find a way to deliver the right content to every user, balancing what they will want with what will provide value over the long term. That’s where machine learning comes in.

On this sponsored episode of the podcast, Ben and Ryan talk with Shrikant Desai and Sowmya Subramanian, two engineering leaders at Warner Bros. Discovery who shape how their ML program figures out what your next favorite show might be. We cover the tools they’ve been using to build their learning pipelines, how a viewer’s history can shape their future, and whether ML algorithms can fill a human curator’s role to surprise and delight viewers.

Episode Notes

Our guests have done most of their ML work on AWS offerings, from AWS Personalize for their initial recommendation engine to SageMaker for model training and deployment pipeline. Now they’re building models from scratch in TensorFlow.

Want to see these recommendations in action? Check out the offerings at Discovery+ and HBOMax.

If you’re a ML/AL data scientist looking to shape the future of automated curation, check out their open roles.

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