 Open Data Hub makes popular AI and machine learning frameworks and tools available through a common UI. But the real power is the integration you get through the use of the deployment operator. Operators have made it far easier to deploy applications to Kubernetes clusters. And the Open Data Hub operator takes out a step further by allowing you to select which tools to deploy and stitching together the common integration points for those applications behind the scenes, such as making a spark cluster available through a Jupyter notebook. We also use Open Data Hub as a framework to enable customers to deploy third-party applications onto their platforms. And as I mentioned before, Red Hat has built an entire technology partner ecosystem on top of Kubernetes. AI and machine learning is represented by over two dozen technology partners, several of which can plug into the Open Data Hub or can be used to extend the core capabilities of the open-source tooling. Red Hat began the Open Data Hub project back in 2016 in response to our desire to apply analytics to our own business processes. Our data scientists needed access to data and tools at scale. Our initial projects targeted improved product quality through the analysis of continuous integration logs, as well as improved customer support through the use of predictive analytics. And it grew from there. When we shared with customers what we were doing, we found we had very similar goals, and they were also interested in how the combination of open-source and open-shift could help provide answers. We incubated the project in our office as CTO and worked with our partners and open-source communities to take on more use cases. Today, our consulting services team has adopted the Open Data Hub project as a way to communicate with customers around the importance of upstream innovation in the AI and machine learning space, as well as how to help them implement their projects on Kubernetes. We are always looking for areas to improve. Right now, one of our core focus areas is helping customers put models into production while ensuring trust, repeatability, and transparency in those models. I expect to continue to see a rise in the importance and transparency required of AI and machine learning. And as a result, you can expect to see more from Red Hat and our partners along these lines in the coming months.