How AWS aims to democratize machine learning with cloud services Posted on : Dec 08 - 2020

Amazon Web Services Inc. has made a big bet on artificial intelligence and machine learning, and just how big a bet is likely to become apparent Tuesday when its AI chief presents his keynote at the cloud giant’s re:Invent virtual conference that continues this week.

Swami Sivasubramanian, vice president of Amazon AI, will hold the first-ever re:Invent keynote on the topic, a clear sign that AWS views AI and machine learning as an area ripe for reinvention. AWS Chief Executive Andy Jassy (pictured) told me that the company’s overall aim is to enable machine learning to be embedded into most applications before the decade is out by making it accessible to more than just experts.

“People hire products and services to do a job,” he said. “They don’t really care what you do under the covers.”

In this third of a four-part series, Jassy provided some hints of what Sivasubramanian will cover in his keynotes, as well as the broader picture of how AWS aims to make AI and machine learning a central part of its cloud offerings and how it’s trying to make it easier to use for mere mortals. The interview is lightly edited for clarity.

Look for more strategic and competitive insights from Jassy in my summary and analysis of the interview, as well as in the first part and the second part, and there’s one more installment coming next week, the final week of re:Invent. And check out full re:Invent coverage by SiliconANGLE, its market research sister company Wikibon and its livestreaming studio theCUBE, now in its eighth year covering re:Invent.

Infusing applications with machine learning

Q: Clearly there’s going to be a lot of machine learning AI in the keynotes, and AWS AI chief Swami Sivasubramanian has his own keynote Dec. 8. What’s the opportunity for AWS in machine learning?

A: Last year the machine learning section of my keynote was 75 minutes. So we thought that maybe it’s time for us to break out machine learning. Swami will do a dedicated machine learning keynote where he’ll have a lot of the machine learning goodies in there. We’re both amazed by the pace with which customers are adopting machine learning in AWS.

If you believe like we do that the majority of applications will be infused with machine learning in five to 10 years, we’re still in the very early days. The way we prioritize what we’re working on breaks out into a few customer asks. One is just help our expert machine learning practitioners more easily build what they need. People are comfortable building the models and training them and tuning them and deploying them. And they want increasing performance across every machine learning framework that matters.

Q: Which frameworks matter the most?

A: If you remember a couple of few years ago, I mentioned in our keynote that while TensorFlow was the framework that seemed to resonate with most people at that point, the one constant in machine learning we were seeing was change. And if you fast forward a couple few years from now, and you look at the usage and maybe even more as a leading indicator, the publication of papers that are built on the different machine learning frameworks, PyTorch is used at least as much as TensorFlow, and 90% of the people who do machine learning use at least two frameworks and 60% use more than two.

It’s still very early in what the frameworks are. We have dedicated teams who do nothing but work just on each of those frameworks that matter to customers, to optimize the performance where you’ll see the performance better running on AWS or anywhere else.

And you’ll see some of those numbers in Swami’s keynote. So first is how to make it easier for expert machine learning practitioners. That’s about the frameworks. That’s about the chips. Like I was talking about Inferentia as an example, to help you do inference more cost effectively and quickly. There just aren’t that many expert machine learning practitioners. And so it never gets extensive in most enterprises if you don’t make it easier for everyday developers and data scientists to use machine learning. View More