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The explosive growth of compute power in cloud data centers is facilitating new applications of machine learning to help companies better leverage their data.
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 3/10, 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.”
“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. 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.”
Full transcript
Laurel Ruma: From MIT Technology Review, I'm Laurel Ruma. This is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace.
Our topic today is machine learning in the cloud. Across all industries, the exponential increase of data collection demands faster and novel ways to analyze data, but also learn from it to make better business decisions. This is how machine learning in the cloud helps fuel innovation for enterprises, from startups to legacy players.
Two words for you: data innovation. My guest is Dr. Bratin Saha, vice president and general manager of machine learning services for Amazon AI. He has held executive roles at NVIDIA and Intel. This episode of Business Lab is produced in association with AWS. Welcome, Bratin.
Bratin Saha: Thank you for having me, Laurel. It's great to be here.
Laurel: Off the top, could you give some examples of how AWS customers are using machine learning to solve their business problems?
Bratin: Let's start with the definition of what we mean by machine learning. Machine learning is a process where a computer and an algorithm can use data, usually historical data, to understand patterns, and then use that information to make predictions about the future. Businesses have been using machine learning to do a variety of things, like personalizing recommendations, improving supply chain forecasting, making chatbots, using it in health care, and so on.
For example, Autodesk was able to use the machine learning infrastructure we have for their chatbots to improve their ability to handle requests by almost five times. They were able to use the improved chatbots to address more than 100,000 customer questions per month.
Then there's Nerd Wallet. Nerd Wallet is a personal finance startup that did not personalize the recommendations they were giving to customers based on the customer's preferences. They’re now using AWS machine learning services to tailor the recommendations to what a person actually wants to see, which has significantly improved their business.
Then we have customers like Thomson Reuters. Thomson Reuters is one of the world's most trusted providers of answers, with teams of experts. They use machine learning to mine data to connect and organize information to make it easier for them to provide answers to questions.
In the financial sector, we have seen a lot of uptake in machine learning applications. One company, for example, is a payment service provider, was able to build a fraud detection model in just 30 minutes.
The reason I’m giving you so many examples is to show how machine learning is becoming pervasive. It's going across geos, going across market segments, and being used by companies of all kinds. I have a few other examples I want to share to show how machine learning is also touching industries like manufacturing, food delivery, and so on.
Domino's Pizza, for example, had a project called Project 3/10, where they wanted to have a pizza ready for pickup within three minutes of an order, or have it delivered within 10 minutes of an order. If you want to hit those goals, you have to be able to predict when a pizza order will come in. They use machine learning models to look at the history of orders. Then they use the machine learning model that was trained on that order history. They were then able to use that to predict when an order would come in, and they were able to deploy this to many stores, and they were able to hit the targets.
Machine learning has become pervasive in how our customers are doing business. It's starting to be adopted in virtually every industry. We have more than several hundred thousand customers using our machine learning services. One of our machine learning services, Amazon SageMaker, has been one of the fastest growing services in AWS history.
Laurel: Just to recap, customers can use machine learning services to solve a number of problems. Some of the high-level problems would be a recommendation engine, image search, text search, and customer service, but then, also, to improve the quality of the product itself.
I like the Domino's Pizza example. Everyone understands how a pizza business may work. But if the goal is to turn pizzas around as quickly as possible, to increase that customer satisfaction, Domino's had to be in a place to collect data, be able to analyze that historic data on when orders came in, how quickly they turned around those orders, how often people ordered what they ordered, et cetera. That was what the prediction model was based on, correct?
Bratin: Yes. You asked a question about how we think about machine learning services. If you look at the AWS machine learning stack, we think about it as a three-layered service. The bottom layer is the machine learning infrastructure.
What I mean by this is when you have a model, you are training the model to predict something. Then the predictions are where you do this thing called inference. At the bottom layer, we provide the most optimized infrastructure, so customers can build their own machine learning systems.
Then there's a layer on top of that, where customers come and tell us, "You know what? I just want to be focused on the machine learning. I don't want to build a machine learning infrastructure." This is where Amazon SageMaker comes in.
Then there's a layer on top of that, which is what we call AI services, where we have pre-trained models that can be used for many use cases.
So, we look at machine learning as three layers. Different customers use services at different layers, based on what they want, based on the kind of data science expertise they have, and based on the kind of investments they want to make.
The other part of our view goes back to what you mentioned at the beginning, which is data and innovation. Machine learning is fundamentally about gaining insights from data, and using those insights to make predictions about the future. Then you use those predictions to derive business value.
In the case of Domino's Pizza, there is data around historical order patterns that can be used to predict future order patterns. The business value there is improving customer service by getting orders ready in time. Another example is Freddy's Frozen Custard, which used machine learning to customize menus. As a result of that, they were able to get a double-digit increase in sales. So, it's really about having data, and then using machine learning to gain insights from that data. Once you've gained insights from that data, you use those insights to drive better business outcomes. This goes back what you mentioned at the beginning: you start with data and then you use machine learning to innovate on top of it.
Laurel: What are some of the challenges organizations have as they start their machine learning journeys?
Bratin: The first thing is to collect data and make sure it is structured well—clean data—that doesn't have a lot of anomalies. Then, because machine learning models typically get better if you can train them with more and more data, you need to continue collecting vast amounts of data. We often see customers create data lakes in the cloud, like on Amazon S3, for example. So, the first step is getting your data in order and then potentially creating data lakes in the cloud that you can use to feed your data-based innovation.
The next step is to get the right infrastructure in place. That is where some customers say, "Look, I want to just build the whole infrastructure myself," but the vast majority of customers say, "Look, I just want to be able to use a managed service because I don't want to have to invest in building the infrastructure and maintaining the infrastructure,” and so on.
The next is to choose a business case. If you haven't done machine learning before, then you want to get started with a business case that leads to a good business outcome. Often what can happen with machine learning is to see it's cool, do some really cool demos, but those don’t translate into business outcomes, so you start experiments and you don't really get the support that you need.
Finally, you need commitment because machine learning is a very iterative process. You're training a model. The first model you train may not get you the results you desire. There's a process of experimentation and iteration that you have to go through, and it can take you a few months to get results. So, putting together a team and giving them the support they need is the final part.
If I had to put this in terms of a sequence of steps, it's important to have data and a data culture. It’s important in most cases for customers to choose to use a managed service to build and train their models in the cloud, simply because you get storage a lot easier and you get compute a lot easier. The third is to choose a use case that is going to have business value, so that your company knows this is something that you want to deploy at scale. And then, finally, be patient and be willing to experiment and iterate, because it often takes a little bit of time to get the data you need to train the models well and actually get the business value.
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