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7 Things to Know About AWS ML: Swami Sivasubramanian

‘Advancements in machine learning, fueled by scientific research, abundance of compute resources and access to data, has … meant that machine learning is going mainstream,’ says Sivasubramanian, vice president of artificial intelligence and machine learning at Amazon Web Services. ‘We see this in our customers’ adoption of the machine learning technology.’

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More than 100,000 Customers Use AWS For ML

Scientific work in ML is exploding, and the publication of scientific papers has grown exponentially over the past years, with an estimated 100-plus published per day, according to Sivasubramanian.

“Advancements in machine learning, fueled by scientific research, abundance of compute resources and access to data, has also meant that machine learning is going mainstream,” Sivasubramanian said. ”We see this in our customers’ adoption of the machine learning technology. More than 100,000 customers use AWS for machine learning, and machine learning is enabling companies to reinvent entire tranches of their business.

Those customers include Basel, Switzerland-based Roche, the world’s second largest pharmaceutical company, which uses Amazon SageMaker to accelerate the delivery of treatments and tailor medical experiences.

To help its customers “cure choice paralysis” by offering tailored content suggestions, the Discovery+ streaming service uses Amazon Personalize, an ML service—based on the same technology used at Amazon.com—that makes it easy for developers to add individualized, real-time recommendations for customers using their applications. Real-time analyses features of Contact Lens—an Amazon Connect feature that allows users to analyze conversations between customers and agents by using speech transcription, natural language processing and intelligent search capabilities—is used by The New York Times to respond to customer issues in the moment. And automaker BMW Group is using Amazon SageMaker to process, analyze and enrich petabytes of data to forecast the demand of vehicle model makes and individual equipment on a worldwide scale.

“We can see right before our eyes that machine learning science is translating to real customer adoption, and that’s not by accident,” Sivasubramanian said, noting Amazon’s customer-obsession leadership principle of working backward from customers’ problems to develop technology to address them.

“Our teams of researchers are usually embedded in the business, so science goes directly to the customer,” he said. ”Researchers that come to AWS and Amazon want to help create powerful innovations that impact millions of people and make machine learning accessible to every organization.”

 
 
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