In the past decade, machine learning has become a familiar technology for improving the efficiency and accuracy of processes like recommendations, supply chain forecasting, developing chatbots, image and text search, and automated customer service functions, to name a few. Machine learning today is becoming even more pervasive, impacting every market segment and industry, including manufacturing, SaaS platforms, health care, reservations and customer support routing, natural language processing (NLP) tasks such as intelligent document processing, and even food services.

Take the case of Domino’s Pizza, which has been using machine learning tools created to improve efficiencies in pizza production. “Domino’s had a project called Project 3TEN which aimed to have a pizza ready for pickup within three minutes of an order, or have it delivered within 10 minutes of an order,” says Dr. Bratin Saha, vice president and general manager of machine learning services for Amazon AI. “If you want to hit those goals, you have to be able to predict when a pizza order will come in. They use predictive machine learning models to achieve that.”

The recent rise of machine learning across diverse industries has been driven by improvements in other technological areas, says Saha—not the least of which is the increasing compute power in cloud data centers. 

“Over the last few years,” explains Saha, “the amount of total compute that can be thrown at machine learning problems has been doubling almost every four months. That's 5 to 6 times more than Moore's Law. As a result, a lot of functions that once could only be done by humans—things like detecting an object or understanding speech—are being performed by computers and machine learning models.”

And although advances in technology are an incentive to innovate, focusing on customer needs is key. Saha continues, “At AWS, everything we do works back from the customer and figuring out how we reduce their pain points and how we make it easier for them to do machine learning.” The goal is to reach a point where it’s less expensive and machine learning is faster. So with AWS Saha explains, “At the bottom of the stack of machine learning services, we are innovating on the machine learning infrastructure so that we can make it cheaper for customers to do machine learning and faster for customers to do machine learning. There we have two AWS innovations. One is Inferentia and the other is Trainium.”

The current machine learning use cases that help companies optimize the value of their data to perform tasks and improve products is just the beginning, Saha says.

“Machine learning is just going to get more pervasive. Companies will see that they're able to fundamentally transform the way they do business. They’ll see they are fundamentally transforming the customer experience, and they will embrace machine learning.”

Show notes and references

·      AWS Machine Learning Infrastructure

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