Amazon Sees Increased Adoption of Machine Learning
Amazon Web Services (AWS) grew in Amazon’s second quarter 2019 as it saw a pick up from customers and their increased pace of enterprise migrations, and an increased adoption of services, especially AWS’s machine learning services, according to Amazon CFO Brian Olsavsky.
Unlike other retailers, Amazon can lose money on shipping and still turn a profit on its AWS division, which comes in handy as shipping expenses have taken their toll on the bottom line.
Amazon reported AWS net sales grew to $8.3 billion in Q2 from $6.1 billion a year ago, while AWS operating income increased from $1.6 billion to $2.1 billion. The AWS run rate grew 37%, from $24 billion to $33 billion, year-over-year.
“The $9 billion that we increased our run rate by was second only to Q4 of last year as far as our history,” Olsavsky said in the retailer’s recent earnings call.
Olsavsky noted AWS is being chosen as a partner to many companies because of its leadership position in technology, vibrant partner ecosystem, and the stronger security it offers.
He also said Amazon is seeing a lot of increased adoption of machine learning services, especially Amazon Sage Maker, which provides developers and data scientists with the ability to build, train, and deploy machine learning models quickly.
“We've had tens of thousands of customers who are now using AWS machine learning services and we'll continue to innovate on behalf of those customers. We released more than 200 machine learning features and capabilities in 2018 alone in this area. Database is also a multibillion-dollar business propelled by Aurora. So we're seeing a lot of strength. We're seeing strong usage growth that outpaces revenue growth as usual increased pace of enterprise migrations.”
To help retailers, AWS launched Amazon Personalize during the quarter, which brings the same machine learning technology used by Amazon.com to engage with shoppers to AWS customers. The service enables AWS customers to develop applications with a wide array of personalization use cases, including specific product recommendations, individualized search results, and customized direct marketing — with no machine learning experience required.
Here’s a recap of Amazon’s other machine learning moves in the quarter:
- AWS announced the general availability of Amazon Textract, a fully-managed machine learning service that automatically extracts text and data, including from tables and forms, in virtually any document without the need for manual review, custom code, or machine learning experience. With Amazon Textract, customers can more easily and accurately process millions of document pages in just a few hours, significantly lowering document processing costs, and allowing customers to focus on deriving business value from their text and data instead of wasting time and effort on post-processing.
- Amazon pledged to upskill 100,000 of its employees across the U.S. by 2025, dedicating over $700 million to provide employees across its corporate offices, tech hubs, fulfillment centers, retail stores, and transportation network with access to training programs that will help them move into more highly-skilled roles within or outside of the company. Programs include Machine Learning University, Amazon Technical Academy, and Career Choice.
- AWS announced Emirates NBD, a leading bank in the Middle East, is using AWS machine learning services to build a personalized retail customer banking experience.
- Amazon hosted thousands of attendees at re:MARS, a new Artificial Intelligence (AI) conference focused on machine learning, automation, robotics, and space. There were over one hundred talks, sessions, and workshops held by Amazon and industry leaders.