The latest News and Information on DevOps, CI/CD, Automation and related technologies.
In a previous post, we talked about the increasing adoption of Platform Engineering teams. The post covered topics such as defining Platform Engineering and the roles and responsibilities of the team. When building an internal platform, a clear goal that many teams want to achieve is: Even though this is key to a successful platform team, this responsibility increases complexity, costs, support time, and more. Not to mention that this can be a long, very long journey.
This is the fourth (and second-last) part of our FinOps series - Optimize. Here is a list of posts in the series: Note: I am ex-AWS, so you will notice a lot more focus on AWS tools and services as examples here, however all cloud providers have similar services and tools.
Matt and I are out in Los Angeles this week for KubeCon 2021 this week. At the GitOpsCon event Tuesday we were excited to attend this Kubernetes session: GitOps in the Real World: Opportunities for Developer Experience Improvement.
The link between DevOps and artificial intelligence for operations (AIOps) has only started to become clear within the last few years. Monitoring and alerting has evolved from a "black box approach," where you don't actually know what's happening, into observability, where you have access to data that provides everything you possibly need to know about your IT systems. How does AIOps come into play? AIOps is the practice of applying artificial intelligence, machine learning, and advanced analytics to automate and improve IT operations. Since it entered as a formal discipline with Gartner in 2016, IT teams have been trying to figure out how to employ it to make their lives easier.