The latest News and Information on DevOps, CI/CD, Automation and related technologies.
Do you want to build software faster and release it more often without the risks of negatively impacting your user experience? Imagine a world where there is not only less fear around testing and releasing in production, but one where it becomes routine. That is the world of feature flags. A feature flag lets you deliver different functionality to different users without maintaining feature branches and running different binary artifacts.
The wait for the latest macOS 14 update is finally over. The newest macOS Sonoma update comes with a plethora of security and privacy features intended to make your computing environment safer. Apple users can now explore new video conferencing features and advanced game mode, enable password and passkey sharing, and so much more. While there’s plenty of excitement that comes with an update like this, it’s important to proceed with caution.
Backstage is a platform for building developer portals. Originally developed internally at Spotify, it’s now open source and available through GitHub. Backstage allows DevOps teams to create a single-source, centralized web application for sharing and finding software (through the software catalog feature), as well as templates and documentation.
At Datadog, we have always been deeply involved with open source software—producing it, using it, and contributing to it. Our Agent, tracers, SDKs, and libraries have been open source from the beginning, giving our customers the flexibility to extend our tools for their own needs. The transparency of our open source components also allows them to fully audit the Datadog software that is running on their systems. But our commitment to open source only starts there.
I used to think my job as a developer was done once I trained and deployed the machine learning model. Little did I know that deployment is only the first step! Making sure my tech baby is doing fine in the real world is equally important. Fortunately, this can be done with machine learning monitoring. In this article, we’ll discuss what can go wrong with our machine-learning model after deployment and how to keep it in check.
