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[MUSIC] >> Hi, I'm Brendan Burns for Microsoft Azure, and I'm going to talk to you about scenarios for Kubernetes.

So traditionally of course, Kubernetes has been thought of as a platform for delivering microservices, services that are connected to one another probably serving some sort of web-based application.

But the truth is that Kubernetes is turning into a broader platform than just that.

One of the most obvious initial uses is to say, "Hey, you know what? Most web-based applications have a diurnal peak." During the day when everybody is awake, there's a lot of traffic, but at night there tends to be a lot less traffic.

So one of the ways you can take advantage of Kubernetes is, sure, deploy your microservices-based application.

But if you need to do some sort of batch analytics, you can also deploy that into the cluster during the times when there's less traffic and therefore less need of the microservices that are delivering your services.

But what we're also seeing is that the Kubernetes API itself is being extended with things like jobs and scheduled jobs to make running those sorts of applications, those sorts of batch processes even easier.

In addition to that, we're actually seeing the orchestration capabilities of Kubernetes being taken advantage of to deliver something like workflow.

So in workflow, you may do something like, say, "Hey you know what? I had to run this job first and then it's going to trigger this job, followed by these two jobs," and we're seeing applications like Brigade which you can find at brigade.sh, rising up to give you an easy orchestration.

So you can file a Javascript program that can define this workflow on top of that Kubernetes cluster.

In addition to that, we're actually seeing scenarios where the complexity of the application is made significantly easier by the knowledge that every single person has access to the Kubernetes API, either via a Cloud provider like the Azure Kubernetes Service or an on-premise installation.

Things like TensorFlow or other AI applications are simply assuming that you have Kubernetes, and so you have that Kubernetes API, and then you're seeing TensorFlow install on top of that to do machine learning and other AI.

Then also, this is for model-building but likewise turning around and using that Kubernetes API for model serving also.

This may not even be in the context of these batch jobs in diurnal peaks.

People are often times building dedicated Kubernetes clusters with GPU or other hardware like FPGA.

Kubernetes is actually learning how to manage these resources so that if you have a limited set of resources, you can ensure that you're not over-scheduling your GPU or you're not over-scheduling an FPGA.

Likewise, if you actually do want to build a hybrid cluster, you can have part of your cluster dedicated to GPU and part of your cluster dedicated to more traditional serving workloads.

Kubernetes can be responsible for landing the right jobs in the right places.

So these are just a few of the scenarios where people are finding an opportunity for Kubernetes to make their lives easier and their application deployment more straightforward.[MUSIC].