Operations | Monitoring | ITSM | DevOps | Cloud

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.

If you’re just starting out in the world of incident response, then you’ve probably come across the phrase “post-mortem” at least once or twice. And if you’re a seasoned incident responder, the phrase probably invokes mixed feelings. Just to clarify, here, we’re talking about post-mortem documents, not meetings. It’s a distinction we have to make since lots of teams use the phrase to refer to the meeting they have after an incident.

In this post, we’re going to take a close look at IIS (Internet Information Services). We’ll look at what it does and how it works. You’ll learn how to enable it on Windows. And after we’ve established a baseline with managing IIS using the GUI, you’ll see how to work with it using the CLI. Let’s get started!