I will introduce two AWS services: CodeGuru and DevOps Guru.
CodeGuru Reviewer uses ML and automated reasoning to automatically identify critical issues, security vulnerabilities, and hard-to-find bugs during application development.
DevOps Guru analyzes data like application metrics, logs, events, and traces to establish baseline operational behavior and then uses ML to detect anomalies. It does this by having the ability to correlate and group metrics together to understand the relationships between those metrics, so it knows when to alert.

Target Audience: Developers, Architects, Decision Makers
Prerequisites: Basic understanding of the code quality metrics and observability
Level: Basic

Extended Abstract
In this talk I will introduce two AWS services: CodeGuru and DevOps Guru.
Code reviews are one example and are important to improve software quality, software security, and increase knowledge transfer in the teams working with critical code bases. Amazon CodeGuru Reviewer uses ML and automated reasoning to automatically identify critical issues, security vulnerabilities, and hard-to-find bugs during application development. CodeGuru Reviewer also provides recommendations to developers on how to fix issues to improve code quality and dramatically reduce the time it takes to fix bugs before they reach customer-facing applications and result in a bad experience
Amazon DevOps Guru analyzes data like application metrics, logs, events, and traces to establish baseline operational behavior and then uses ML to detect anomalies. The service uses pre-trained ML models that are able to identify spikes in application requests. It does this by having the ability to correlate and group metrics together to understand the relationships between those metrics, so it knows when to alert and when not to.