Yeah, so let me let let me take my first analogy to software, right? And I do multiple of these and doesn't mean, we have to write about these analogies, just let's let let let lets me communicate that a lot easier, right. So if if some of you are as old as I am, I think men software used to be written earlier. It used to be what we call waterfall, right? So somebody would write the software, wherever the software engineer word, he or she would then hand it over to a person who is the IT person, let's say, and they would then then deploy it and operate it. And then if a problem happens, some kind of emails or whatever else might happen, etc, right. And this is the reason it was waterfall, not because people didn't know about how to build software faster, it just took very long. And every time you change software, this process needed to be repeated. So rather than doing it on a continuous basis, because I'll do it once a year, right, that's what really created this tendency to have slow releases. Right? And clearly, that is not the case today, right? We have technologies, or the people are doing things like DevOps, right, and they are additional things. But the whole point being is how do you put technology in the middle? Not, you're clearly not removing the people who build the software, the software developers are still there, you clearly don't are not removing the IP folks to operate that. But you are making it smoother using technology to do this handoff, not just the hand of one way, but having it bi directional, right. So you move the software to production, and then production has problems. How do you how do you surface those problems to the people who have to fix them? Right. And there are other things that have happened over time, like cyber was not a big issue a decade ago, it's a huge issue now. So there is an equal in technology that has emerged to add security on top of this, right? There's another thing that has happened, like Cloud has come up was Wasn't there a few decades back? How do you actually cloud brings its own challenges, right, things like that. So ML ops, machine learning is very similar. So how do you actually now put technology in the middle of this collaboration challenge that I described earlier? Again, with the intent not to remove the people, right? DevOps did not remove the people but to make this collaboration much more efficient. Right. So, so what what is what is what is what are the? What are some of the few things that are different about AI and machine learning? Versus software? Right. So that's why in the case of software, we had DevOps, why isn't DevOps sufficient? Why do we need something like ML ops, which is what Whina is bringing to the table? I won't, I won't find out everything. But I think it just the complexity is much, much, much higher than software. Because one thing that is not as important in case of traditional software is how you manage data. Right? How do you work with data? Right? And that's one thing that requires as a newer capability that doesn't exist today. Right? I already talked about this aspect about risk. The traditional software, people don't talk about risk. And one thing that's very unique about, again, machine learning versus traditional software is that you can put a model in production, but it might find you might find out a month later that something has changed, right? Like we all familiar with what happened during COVID, like my nest thermostat stopped working. Because it was lower the temperature thinking that nobody's at home, which was okay, pre COVID. But no longer true, even though the thermostat is not seeing anybody move around the house, we all sitting and talking to each other here. Right? So. So the point being is that, how do we address issues, like risk, the connectivity with data, there are other things in the platform. We'll talk about optimization in a minute, where that comes in.