Video: How IRIS.TV Implements Machine Learning in Production Environments
Learn more about real-world video AI implementations at Streaming Media's next event.
Watch the complete video of this panel, AI104A: How AI is Revolutionizing Publishing, in the Streaming Media Conference Video Portal.
Read the complete transcript of this clip:
Field Garthwaite: I'm going to quickly cover, from a high level, how we are implementing machine learning in production environments.
The first is creating a common data model. This is something that, for example, Facebook, Google, Amazon, Netflix, they're very good at doing this, but kind of traditional media companies have been a little bit slow to adopt.
The second component, which the video touched on, is how our APIs implement into video players to actually capture audience behavior. What we'll touch on briefly is how we do that in an GDPR-compliant way, which is a very relevant topic for those of you dealing with it right now. Lastly, how you essentially develop insight and kind of get it to teams in an actual way.
The first component here we're going to talk about is metadata ingestion and taxonomy creation, and then we'll kind of move through those other steps. The major takeaway here, in terms of creating a common data model, is that every business is unique. If you're a broadcaster, a news publisher, you may have some common ground with other news publishers and best practices you can adopt. But you're also going to have topics, like sections of your paper or areas that you have specific coverage.
There's a number of examples that Kara will talk about, but one is like the Olympics or other kind of special series. Having a taxonomy and common data model around that makes the data model machine learning manipulable in the future, so you can actually structure business rules around it.
The second piece, the API integration, is fairly straightforward, like most APIs. But IRIS.TV kind of sits on top of any player. In this use case, this is kind of a Brightcove backend. IRIS.TV is also able to implement API installation, so if you have your own video player as Gannett does now, then it's also kind of easy to integrate.
Finally, in terms of how this personalizes video, a little bit of background: Setting up instruction and using NLP to create that contextual kind of relevant data on an asset level sets up the first kind of machine learning system, as well as what device, time of day, other contextual information, and finally cohort analysis.
Those three sit under a business-rules engine that allows an editorial team or product team, like Gannett and USA Today, to control the machine learning.
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