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HITS Spring: M&E Companies Can Significantly Benefit from Machine Learning

Media and entertainment companies stand to benefit tremendously from the use of machine learning (ML), as well as artificial intelligence (AI) and deep learning, but not all ML and AI platforms are created equal and each organization needs to find the platform that work best for it, according to Usman Shakeel, worldwide technical leader, media and entertainment, at Amazon Web Services (AWS).

As the media industry continues to evolve, “one of the key things that we’re seeing is that, first of all, the divide between the content owner and the consumer is kind of shortening,” he said May 17, speaking during a breakout session at HITS Spring: The Hollywood Innovation & Technology Summit.

“Consumers are really demanding that they want to watch … content anytime, anywhere, anyhow they want,” he said during a session called “The Evolution of Content Production Aided by Machine Learning”

From a media perspective, there used to be more “intermediaries” between the content owner and the viewer, he noted. But, thanks in part to the growth of over-the-top (OTT) TV services, the number of intermediary services is “shrinking” and the consumer is “now being more directly involved with the content itself,” he said. That is, in turn, creating a need for “more and more personalization [and] more automation,” he said, adding this is all having a “pretty big impact in terms of how the production companies” are producing content, he said.

And, as a result, now more than ever, “you want to be able to create and curate content more efficiently with the help of machine learning,” he told the summit. “If you want to distribute or deliver content anywhere, anytime then you have to scale the supply chain,” he said.

There is, after all, “a lot of information that you can get from the way that somebody is watching that content, the sentiment analysis” and social media, and “now there is all that data,” he explained. The challenge is figuring out how do to “make sense of the data,” and ML can help accomplish that, he said.

“Our vision within AWS is that we want to put machine learning in the hands of basically any developer and every data scientist out there…. The way we do that is by offering multiple different flavors of machine learning to our customers,” depending on their specific needs, he said.

Pointing to the “challenges within deep learning,” he said: “What we see is there is a lot of stuff out there, from different frameworks to different models to data sets, etcetera. But really, it’s about scalability, flexibility and training.”

He went on to tell the summit: “We believe that there needs to be a lot of flexibility within [the] ML space. Why? Because anytime you’re trying to understand the answer to a problem … it’s going to really” come down to each “specific business case, which is the data set.”

As an example, he pointed to the recent example of a customer that wanted a solution for closed captioning. One challenge that needed to be figured out was how to “put the caption within the screen,” he noted, adding: “Creating a generic solution is almost impossible.”

Although “we can say that we’ve made a lot of progress with respect to machine learning and deep learning and all of those great things,” he said: “There is still that human factor that kind of comes into play.” Because there are “different requirements in each case,” depending on a customer’s needs, “it’s very important” to factor in the specific “use-case scenario” when trying to pick a platform for ML, he said.

He went on to explain how Amazon SageMaker can be especially useful for M&E companies that want to use ML. That end-to-end ML platform was introduced by the company last year. Developers can significantly decrease their “heavy lifting” by using SageMaker for ML, Dan Mbanga, AWS global lead business development manager of Amazon AI Platforms, said at the recent AI Conference in New York. (https://www.mesaonline.org/2018/05/01/aws-three-major-trends-as-customers-adopt-machine-learning/) While introducing SageMaker last year, AWS CEO Andy Jassy said that service was “an easy way to build, train and deploy machine learning models for everyday developers.”

HITS Spring was produced by the Media & Entertainment Services Alliance (MESA) and the Hollywood IT Society (HITS), in association with Women in Technology: Hollywood (WiTH); the Content Delivery & Security Association (CDSA) and the Smart Content Council. The event was presented by Entertainment Partners, with sponsorship by Expert System, LiveTiles, Microsoft Azure, Ooyala, Veritone, Amazon Web Services, Avanade, Avid, IBM Security, MarkLogic, Aspera, Light Point Security, MicroStrategy, SAS, Scaeva Technologies, Western Digital, Brainstorm, Zaszou IT Consulting and Bob Gold & Associates.

To access the AWS presentation, click here.