UPDATED 16:06 EDT / DECEMBER 03 2019

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AI takes center stage: AWS debuts SageMaker Studio, CodeGuru, Kendra and more

Kicking re:Invent into high gear, Amazon Web Services Inc. today unveiled a massive array of artificial intelligence solutions spanning more than a half-dozen product categories, from code debugging to cybersecurity and contact center automation. And that’s on top of a formidable lineup of infrastructure announcements. 

The new AI offerings fill in key pieces of the product vision AWS Chief Executive Andy Jassy outlined during his keynote address this morning. The provider’s objective, he said, is to deliver a broad portfolio of machine learning tools spanning multiple layers of abstraction. The foundational layer is the freshly upgraded SageMaker developer toolkit, while the top layer includes pre-packaged AI applications such as Kendra and Amazon Fraud Detector. 

SageMaker gets smarter

SageMaker, the provider’s managed toolkit for building, training and deploying machine learning models, is the AI service that has received by far the most new features today. The main highlight is a web-based integrated development environment called SageMaker Studio in which data scientists can develop neural networks.

Building an AI usually requires engineers to toggle between several different coding tools. SageMaker Studio provides the ability to write code, experiment with model changes, visualize data and perform debugging in a single interface. It furthermore provides access to the other new development tools AWS debuted for SageMaker at re:Invent.

SageMaker Studio’s debugging features are powered by an AI glitch-hunting tool that engineers can customize to track down specific issues. In addition, there are new features that pick out the best neural network for a given project, prepare AI training datasets and manage experiments. Finally, there’s an open-source toolkit that will enable developers to build graph-based neural networks for use cases such as chemical analysis.

Amazon Augmented AI

Companies using machine learning to process sensitive data, such as mortgage applications, have to take into account that their AI will sometime get tripped up. A mortgage application may contain unclear handwriting or might have poor resolution. Organizations typically deal with the issue by forming a team of reviewers to double-check their models’ decisions.

Amazon Augmented AI, which also debuted today, makes it easier to manage human reviews. It allows developers to build rules into their AI applications that automatically determine when decisions should be routed to humans for a second opinion. 

CodeGuru 

CodeGuru is another AI service aimed at developers, but it can be used for projects other than machine learning applications as well. CodeGuru looks at new code produced by engineers and suggests improvements that can enhance software efficiency.

AWS said that the service generates suggestions by drawing on patterns extracted from thousands of internal Amazon.com Inc. software projects, as well as GitHub repositories. The company’s developers use it to free up time normally spent on manual code reviews. In one instance, AWS said, one of its engineers cut a service’s processor utilization by over 50% after CodeGuru flagged an inefficient snippet of code. 

Kendra

Moving up the stack, AWS introduced Kendra, a search engine that allows companies to make their internal data troves easier to sift through. Kendra allows users to centrally search across SharePoint deployments, Dropbox folders, knowledge bases and other systems using natural-language questions.

The service’s machine learning algorithms can do more than just fetch a list of matching results. In some cases, if a user asks a specific question like “how much is the cash reward on the corporate credit card,” Kendra is capable of providing an equally specific answer in the form of a single word or phrase.

Amazon Fraud Detector

Amazon Fraud Detector makes the technology that the retail giant uses to catch illicit transactions on its marketplace available through an application programming interface. AWS is touting it as a better alternative to traditional scammer-catching systems, which require companies to manually set up rules for what transactions to block. 

Under the hood, Amazon Fraud Detector works by essentially customizing itself for every organization. Companies can submit transaction records and specify the most common types of fraud they deal with. From there, Amazon Fraud Detector determines which one of its built-in detection algorithms is most suitable for the task, then trains it on the customer’s data.

Amazon Detective 

Like Fraud Detector, the newly unveiled Amazon Detective is designed to detect suspicious activity, but it focuses not on illicit transactions but rather potential breaches in a company’s AWS environment. The service analyzes operational logs from cloud resources to create what the provider describes as a unified picture of activity.

Amazon Detective is meant to serve as an investigative tool for security teams tasked with looking into suspicious incidents. There are analytics features for drilling into incident data along with visualizations that enable users to see key information at a glance.

Contact Lens for Amazon Connect

Capping off the list of major AI announcements from re:Invent is Contact Lens. It’s a set of analytics features for Amazon Connect, a service that enterprises use to manage the contact centers through which they process customer inquiries. 

Contact Lens allows companies to find patterns in “the content, sentiment and trends of their customer conversations to identify crucial customer feedback and improve customer experience,” explained AWS  product manager Atul Deo. “Contact Lens for Amazon Connect will allow supervisors to get a real-time view of live customer interactions, including real-time customer sentiment progression and alerting based on pre-defined words and phrases.”

Photo: AWS

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