Curated articles, resources, tips and trends from the DevOps World.
During this past AWS re:Invent, Amazon CEO Andy Jassy shared valuable lessons learned from Amazon’s own experience developing nearly 1,000 generative AI applications across the company.
Infrastructure as Code (IaC) has gone through fascinating shifts over the years. The evolution of infrastructure management has been a story of constant iteration, shaped by the needs of scaling systems, velocity and safety metrics a lá DORA, and the demands of developer productivity.
Devtron sponsored this post. Insight Partners is an investor in Devtron and TNS. There are many factors to consider when deploying application workloads.
When I became a programmer many moons ago, the last thing I ever thought I’d need to know about was intellectual property (IP) law. Oh well, more fool me.
Most modern software is built on a standard three-tier architecture — sometimes referred to as the REST architecture — composed of a client, an API server that exposes APIs that the client calls, and a database.
Nothing lasts forever, and this especially true in open source software. Even the most popular versions of open source technologies will one day reach their end-of-life (EOL).
“Despite all our unit and integration tests passing, we’re seeing a lot of broken flows in staging. Teams spend days debugging there.” This confession from a VP of engineering at a fintech company with more than 100 microservices captures a familiar frustration.
Over the past couple of years, there has been extensive discussion of how generative AI can benefit DevOps practitioners.
By using a Docker image, it is not only possible to deploy one container after another, it’s quite easy. Once you’ve pulled the image from a registry (such as Docker Hub), each container can then be deployed with a single dockercommand.
Discover how to identify and manage blockers in DevOps to ensure smooth workflows and enhance team productivity.
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