 easiest job, I have the hardest one because after this, it's evening activities, so this is going to be hard, but in fact, the funny part is I actually have the easiest talk in the world because I'm, David Roncek, I helped found the Kubeflow project, but I basically do nothing. All these people are doing the stuff that makes Kubeflow great, we're just kind of wiring it together. Everyone hears about ML, it's changing the world, dynamics, eating everything, but the problem is that most of the world is like this. There's magical AI goodness on one side and everyone else is on the other side and in between there's just lots of pain. The biggest reason that there is this split between these two opportunities to go out and get all this great stuff and where people are today is because people have been writing these incredibly bespoke solutions for ML that are not composable, they're hard to swap out, the pieces that make sense to you or maybe your organization has changed, they're hard to be portable, meaning move from your laptop to your training rig to your on-prem to cloud number one to cloud number two, wherever the data is, and then finally it's hard to scale, so you might be able to get it running on a single machine, but then to go and do that just like open AI did on 2500 machines is very, very challenging. To dive into each of those very briefly around the composability, people think about ML as just being this model, but in fact that's not what it is at all, it's all these other things that end up being rounded and those other things are the things that people, great companies and projects have solved. Again, like the folks up here, Pakaderm is doing the data analysis portion, Jupiter is doing the interactive research, Selden doing great serving and other tooling, and today it's very hard to tie all those components together. Similarly portability, once you get your stack up and running on top of Kubernetes, it may be made up of this many layers or more, and when I talk about that pipeline earlier, that may just be that top portion, let alone everything that's below it, and then you go to your training rig and it's something completely different, and then you go to your cloud and it's something completely different again, and you're hit over and over and over again with the various, you know, reset up and differences between those environments. And then finally, scalability, you know, I mentioned already scaling via nodes, that is one type of scalability, there are other scales, there's how do you scale the number of experiments that you run, how do you scale your teams, how do you scale your data, all these various things, those components are involved in scalability as well. So, you know, containers in Kubernetes are pretty good at solving this, but the problem is that you end up having to become an expert in a whole bunch of things as it stands today, which is not great. So, that's why we introduce Kubeflow. How can we make this overall system much easier for you? And our mission here, and I say it over and over again, make it easy for everyone to learn, deploy, and manage portable distributed ML on Kubernetes. That is not us as part of the Kubeflow project writing all this stuff. This is packaging and helping other projects make their services available in a standard based way so that you can swap in and out, so that you can scale them, so that you can move them from place to place. Around that portability component, the way to think about it is that bottom section becomes all Kubernetes, that's the abstraction layer there, and then the section over on the other side becomes Kubeflow, and you're able to stamp out that Kubeflow in every location that you have. Today in the box, and on Friday, don't tell anyone, but we'll be announcing that we've cut our 0.1 release, which we're very proud of. Thank you. But specifically in the box today, we have Jupyter, we have TensorFlow, we have Argo for workloads, we have Selden Core in the box, Daniel is working very hard on a Packarderm proposal that we're very excited about. We have Reverse Proxy via Ambassador, and we'll be talking about all the sorts of things we have. Out of that overall section up there, it's basically these components already have an option in the box, but you can use many more. And we really are just getting started. This is a very small subset of the people who are helping out today, and we're really excited. I happen to be from Kubernetes from, I don't know, day negative 10, and it really feels like that again. There were so many, when we first got Kubernetes up and running, there were so many container solutions, so many orchestration solutions, everyone was just looking for something to rally around, and that's what Kubernetes provided. Kubeflow feels very, very similar. It feels like there's so much activity, and everyone just wants a place, a community, to come together and rally together and form this vision of what we all think is right in the world, and that's what we're trying to do. So thank you very much.