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LAS VEGAS — What is AWS’ end game with AI and machine learning (ML)? Is it to lead the pack with tools and frameworks? Or dominate other players such as Google and Facebook that built powerful ML libraries or frameworks and placed more skin in the game?
No, AWS’ goal with AI and ML is much the same as the company’s overall mission: put the best tools and cloud infrastructure to build applications into the hands of everyone — and they mean everyone.
At the AWS re:Invent 2018 conference here, AWS CEO Andy Jassy said he wanted to empower “builders” with the tools they need to create apps. Not just developers and data scientists, but also IT staff, ops staff, marketing staff, line-of-business folks — even CIOs, CMOs and CEOs should have access to the same tools.
AI and machine learning are no different — the goal is to democratize access to the best tools, said Joel Minnick, senior manager of product marketing for AI and ML at AWS.
“What we don’t want to see is that machine learning is the toolset of the largest, most technically savvy companies,” Minnick said. “If you are a college student with a great idea about how to build a great application, you should have the same toolset to use that the largest technology companies in the world have to use.”
That’s also why AWS is so focused on data for machine learning models. The company’s SageMaker Ground Truth service, unveiled this week, focuses on data preparation and labeling for machine learning models. Because everyone, not just the biggest, richest companies, should have the ability to clean and prepare their data for machine learning models.
In a lunchtime Q&A with reporters, Jassy said AWS customers are hungry for AI and ML. “I think every cloud app, going forward, will have some type of machine learning and AI infused in it,” he said.
Tens of thousands of AWS customers want to use machine learning and they need help at every layer of the ML stack, Jassy said. Moreover, for machine learning to take off in the enterprise, it must become accessible and usable to everyday developers and data scientists. This was AWS’ goal with its SageMaker machine learning service, which the company enhanced significantly here this week.
Amazon doesn’t really play at the AI/ML level — they don’t have a TensorFlow, PyTorch of Caffe2 — so they’re building infrastructure around and below it to make it easier for folks to do AI, and that strategy plays to AWS’ strengths, said Charles Fitzgerald, managing director of Platformonomics, LLC, a Seattle-based strategy consulting practice.
“They lost the great ML framework war, so now they support everything,” he said.
At his annual re:Invent press conference, Jassy responded to a broad range of questions — from AI ethics, to the company’s relationship with the open source community, to how the Amazon HQ2 search and decision plays for AWS and its hiring.
But he didn’t address something I really wanted to know, but wasn’t going to ask in a press conference: What does he think of those 3-8 New York Giants? Jassy once told me he’s a Giants season ticket holder and comes from a family of Giants season ticket holders. Maybe he should sic some of that smart-alecky machine learning on the Giants’ analytics system to generate a winning team again. Saquon Barkley and “OBJ” Odell Beckham Jr. are great players who typically get their stats, but they can’t do it alone.