AWS is on a journey to revolutionize DevOps using the latest technologies. AWS thinks of it this way: code, logs, and application metrics are all data that we can optimize with machine learning (ML).
In this talk I will deep dive into two AWS completely managed Serverless services: CodeGuru and DevOps Guru and explore their capabilities on the concrete practical examples.
Amazon CodeGuru Reviewer uses ML and automated reasoning to automatically identify critical issues, security vulnerabilities, and hard-to-find bugs during application development. I 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, so it knows when to alert and when not to.

AWS is on a journey to revolutionize DevOps using the latest technologies. AWS thinks of it this way: code, logs, and application metrics are all data that we can optimize with machine learning (ML).
In this talk I will deep dive into two AWS completely managed Serverless services: CodeGuru and DevOps Guru and explore their capabilities on the concrete practical examples.