 All right, good morning and welcome everyone. I hope you're all enjoying all these wonderful presentations. Obviously, I am enjoying them. There's going to be many more presentations coming soon. So I am Sold Mollick. I'm the CTO and co-founder here at Spectral Cloud. And I'm delighted to be back here again talking to you about edge computing as it continues to be a true innovation in the Kubernetes ecosystem. All your Spectral Cloud were also very truly honored to be sponsoring this fantastic event. Now, what I'm really excited to share with you guys is the results and findings from our third state of Kubernetes annual report. This research is conducted by an independent research firm, Dimensional Research, who surveyed over 300 large Kubernetes shops. The server conducts questions regarding everything from adoption to usage to drivers of the Kubernetes platform. And before we dive into the results, I just want to share my favorite quote, which was from a IT ops manager who talks about how Kubernetes is their most frustrating, painful, and beautiful thing I've ever worked with in my technology career. I think it's safe to say that all of us feel the same way. So where does edge fit into the survey? Well, about half of the respondents from the survey, again, large enterprise shops in Kubernetes in production, responded that they are already doing edge. In last year's survey, that number was closer to 33%. So significant increase in the adoption of edge Kubernetes projects. This year's survey also covers a more specific question specifically on the level of adoption in edge computing. So out of the organizations doing edge, close to 42% are either partially in production or fully in production at scale, with the remaining 58% already doing field trials or pilots. So what's driving the adoption of edge? Well, the survey found that these organizations are leveraging edge computing to drive real business outcomes. Everything from driving the business improvements and efficiencies, perhaps automating when the French fry machine turns out, based on the number of people coming in, to bringing instantaneous responses to where the users are, to adhere to maybe regulations and compliance for both sensitive and sensitive workloads and data. For example, if you had to do a tumor detection for an MRI scan. And now AI workloads are also increasing the adoption of edge computing. Already, we're seeing a significant increase in the number of AI models that are running at the edge environment. And Gardner is predicting that by 2027, 65% of edge use cases at the edge will be AI. But edge brings its own sets of challenges. Edge computing, let alone edge Kubernetes, is already hard. And we are still in those early teenage years where we have solved for the basics, but now we're really trying to figure out how do we do enterprise class management and scale? So what are the top challenges as the surveys found out? Well, number one was security. These edge devices are out in the wild. So how do you onboard these devices and the entire application stacks securely and also do it in a tamper-proof design? The other three challenges we identified and the survey found are all interconnected. The connectivity challenge is more specifically, how do I manage when connectivity is either intermittent or non-existent? And how do I update my devices without risking downtime? For the cost challenge, right, it's not just the cost of the hardware, but cost in all of its forms. For example, the field engineering cost, every time you have to send a field engineer to one of the edge locations, either for provisioning or a day to update, is quite costly. So all these three challenges that follow security are all interconnected. How do I manage without the expense of sending expensive field engineering to sites where there's no connectivity and no easy remote management? And of course, these challenges just multiply at scale. So how do we as a community get past all these challenges since we're all obviously big believers in edge computing and the promise it will deliver? One thing SpectroCloud is doing is invest in research funds like this one to really help understand the challenges as where they are today. We're also working very closely with the community on practical solutions on these challenges. So earlier this year, we announced a partnership with Intel and our open source Kairas project to provide immutability and hardware-enabled security, everything from the silicone all the way up to the applications. Really taking that idea of having everything tamper-proof from the applications down. SpectroCloud's advanced project team is also exploring with partners and open source alternatives to LCD on how we can provide Kubernetes high availability and resiliency with only two nodes. Obviously resulting in significant cost savings. And now we're incorporating how do we bring all these AI models and AI engines to also run at these edge locations? So managing the entire lifecycle of how your models and engines can run from our palette edge AI capabilities. All around us, organizations are running some really cool and innovative edge Kubernetes projects. To hear more about what they're doing to look at their server results, please download a copy of the independent research, which not only covers edge computing, but it also covers some Kubernetes best practices. You can scan the QR code right behind me here. Also the SpectroCloud booth is right outside. So if you have any questions regarding palette edge AI or two node edge solutions or anything Kubernetes related, we'd love to chat. On behalf of SpectroCloud and the CNCF, we hope you all have a wonderful event and have a great day. Thank you so much.