With so much of our future focused on Machine Learning (ML), it only makes sense that AWS spent a large portion of re:Invent 2019 spotlighting its own ML developments. One of the main focuses was on Artificial Intelligence (AI) services that require no prior ML experience. Two unique ML services that became key focal points at the event were Amazon Kendra and Amazon CodeGuru. Both services focus on ease of usability and aim to produce faster, more accurate results for end users.
With the vast amount of digital content spread across your company, it’s no surprise that employees can become frustrated when trying to search for the information they need. More complications arise when you consider the number of native languages of said employees for multi-national organizations. Thanks to its powerful natural language search capabilities, Amazon Kendra helps end users find their information more easily while using natural language questions, as opposed to simple key words. Gone are the days of sorting and sifting through long lists of links and hoping that one will provide the information you need. With Kendra, users can quickly and easily find whatever they need, be that a precise answer, FAQ, or even an entire document.
Finding and fixing issues with expensive lines of code has been a long-standing pain point for developers. With Amazon CodeGuru, an ML service that automatically reviews code and provides performance recommendations, developers now have an intelligent ML service sidekick to help stop and prevent errors. CodeGuru does a seamless job of pulling and reviewing code in order to provide a detailed assessment that detects incorrect inputs, anomalies, and concurrency issues. Powered by ML, best practices, and lessons learned across millions of code reviews, CodeGuru gives developers the ability to catch problems faster and easier, and then write and commit future code with minimal errors. And with better code comes better, more reliable software.