Amazon’s leveraging machine learning to fight fraud, audit code, transcribe calls, and index enterprise data. Today during a keynote at its Amazon Web Services (AWS) re:Invent 2019 conference, the tech giant debuted Amazon Fraud Detector, a fully managed service that detects anomalies in transactions, and Code Guru, which automates code review while identifying the most “expensive” lines of code. And those just the tip of the iceberg.
With Fraud Detector, AWS customers provide data email address, IP addresses, and other data, along with markers indicating which transactions are fraudulent and which are legitimate. Amazon takes that data and uses algorithms — along with data detectors developed on the consumer business of Amazon’s business — to build bespoke models that recognize things like potentially malicious email domains and IP address formation. It’s all exposed through a private endpoint API, which can be incorporated into services and apps on the end-user and client side.
As for CodeGuru, which comes in the form of a component that integrates with existing integrated development environments (IDEs), it taps AI models trained on over 10,000 of the most popular open source projects to provide an assessment of code as it’s being written. Where there’s an issue, it provides a human-readable comment that explains what the issue is and points it out down to the line. Additionally, CodeGuru finds the most inefficient and unproductive lines of code by creating at profile every five minutes that takes into account things like latency and processor utilization.
Amazon says it’s used CodeGuru internally to optimize 80,000 applications, and that it’s led to tens of millions of dollars in savings. In fact, Amazon says it was able to reduce processor utilization by 325% and save 39% in just a year.
Amazon also took the wraps off of Connect Lens, a call center product that transcribes calls while simultaneously analyzing them. It offers a full text transcription, and it captures things like the sentiment of calls and long periods of silence or agent cross-talk. Plus, it lets managers search the aforementioned transcriptions by keyword for specific phrases and other dimensions.
And Amazon launched Kendra, a new AI-powered service for enterprise search. Once configured through the AWS Console, Kendra leverages connectors to unify and index previously siloed sources of information. Customers answer a few questions about their data and optionally provide frequently asked questions — think knowledge bases and support documentation — and let Kendra build an index using natural language processing to identify concepts and their relationships. Queries can be tested and refined before they’re deployed, and they self-improve over time as the underlying AI algorithms learn ingest new data.
Kendra’s prebuilt web app is designed to be integrated with existing internal apps, and Amazon says it tracks feedback and signals like which links users click on and the searches they perform to improve the underpinning models.