All the sessions from Transform 2021 are available on-demand now. Watch now.
Amazon’s re:Invent 2019 conference is nearly two weeks out, but try telling that to Amazon Web Services (AWS) — it’s unveiling new products left and right. Following on the heels of Alexa on AWS Core and new languages Amazon Translate and Transcribe, AWS today detailed features designed to make adding AI predictions to apps and services easier than before.
Amazon says that machine learning predictions will soon run on unstructured or relational data in Amazon S3 or Aurora, AWS’ cloud-hosted MySQL and PostgreSQL-compatible relational database service. Specifically, customers will be able to train models in Amazon’s SageMaker platform and run predictions against those models with SQL using Aurora or Athena, Amazon’s interactive query service for analyzing data in Amazon S3.
The benefits extend to QuickSight, the AWS component that lets customers create and publish dashboards that spotlight AI insights. With some configuration and the addition of a few statements to SQL queries, QuickSight will visualize and report all model predictions from SageMaker and other AWS machine learning offerings, like Amazon’s Comprehend natural language processing service.
The idea behind the enhancements — which boil down to direct calls from Aurora, Athena, and QuickSight to machine learning services — is to reduce the amount of custom code that must be written, managed, and supported in production. Copying data from stores while transforming it between formats and feeding it to models not only sucks up time, according to AWS principal Matt Asay, but it complicates security and governance.
“[Now,] you don’t need to [make calls] from your application, making it simpler to add … predictions to your applications without having to build custom integrations … [or] learn separate tools,” wrote Asay in a blog post. “Now anyone who can write SQL can make — and importantly, use — predictions in their applications without any custom code.”
Amazon’s investments in AI and machine learning services have accelerated in recent years, chasing after an AI infrastructure market that’s anticipated to be worth $50.6 billion by 2025. The Seattle company says that tens of thousands of customers now use its fully managed products like SageMaker and Comprehend, including the NFL, AstraZeneca, and Celgene, and it says it launched more than 200 machine learning features and capabilities in 2018 alone.
It’s a wise business direction, if you ask analysts like Jason Helstein at Oppenheimer. In a recent report, he noted that AI can drive AWS revenues and margins as its capabilities are gradually embedded into cloud services. To this end, in the third fiscal quarter of 2019, AWS grew 45% in sales to $9 billion, maintaining pole position ahead of Microsoft Azure and Google Cloud and accounting for 13% of Amazon’s total revenue.
VentureBeatVentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact. Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:
- up-to-date information on the subjects of interest to you
- our newsletters
- gated thought-leader content and discounted access to our prized events, such as Transform 2021: Learn More
- networking features, and more