 Start by asking what MLOps is in the first place. That's an important question to ask for two key reasons. It's a very new discipline and it's often confused with a sister discipline that is equally important, yet distinctly different. That discipline is AIOps. AIOps is a series of multi-layered platforms that automate IT to make it more efficient. It's what allows DevOps and data ops teams to boost their IT infrastructure by using techniques like big data, advanced analytics, and machine learning techniques, which we'll get to in a bit. And this is important because there is so much data that is produced and collected today that it's increasingly hard for businesses to clean, analyze, and gain value from it. This is something that is still very common in the enterprise writ large, despite all of the data that is around today and that is increasingly produced on a daily basis. So by helping DevOps and data ops teams choose what to automate from development all the way through to production, this AIOps discipline has the power to help predict performance problems, do root cause analysis, find anomalies in your code, and more. And now we get to MLOps, which is relevant to AIOps, but it's its own distinct beast. MLOps is a multi-disciplinary approach to managing machine learning algorithms as ongoing products, each with its own continuous life cycle. If you're looking for an analogy, it's easiest to think of MLOps as DevOps for machine learning pipelines. It's really a collaboration between data scientists and engineers and your operation side of the house. And done well, MLOps gives members of both teams and really people across your business or project more shared clarity on machine learning initiatives. It's a way to help your teams choose which tools, techniques, and documentation will help you all reach that elusive stage of production. And I say elusive because that's the end goal of any machine learning initiative. And yet research shows that just 13% of ML projects actually make it to production. So done right, MLOps is a great way to help teams get there. And the crucial thing to reinforce here is that the tools really are secondary to the culture you create and really making it a cross-functional initiative that you want to build within your open source project or your enterprise. So if you are ever in doubt about the difference between AIOps and MLOps, and if you take one thing away from this talk, I want you to remember that AIOps automates machines and MLOps standardizes processes. They're two very different things. And so if you're on a DevOps or a DataOps team, you can and should consider using both depending on your needs. Just don't confuse them for the same thing. Thank you. Any questions? Yeah. So DataOps is really, I would say, for more enterprises and projects that are more advanced in their data maturity. So I would say DataOps is really when you have the people in roles like chief data officer, data scientist, data engineers and analysts who can fill pretty distinct roles in terms of how they use data. And so again, going back to the DevSecOps comparison, it's that shared emphasis on tools, techniques, and people, but I would say it's more concentrated within a data specific part of your business or project. I would also say DataOps is probably pretty rare in this current world we live in because data use and maturity is still shown in surveys to be quite immature. So I think if you're in a business and you're not sure really which type of team you're on, it's most likely that a DevSecOps or DevOps approach would be more along the lines of what you can realistically do. But DataOps is more of an analogy if you are on that higher stage of maturity. I think we're good.