Well, I think I would say First of all, there's no standard path to where people end up in their started journey. Some people started, started in 18 50 and everyone can have a equally good challenge. Success a songs you get started At some point I got here, I studied linguistics. So right off the bat, not a traditional start, Um, and then for about a year, I wouldn't really want to do at the problem. So I moved to Sweden either and did a master's degree in marketing. Uh huh. And then got a consulting job with Bain and Company during school and then that broke him offer back with, um, worked in management consulting for a few years assembled, who chose, well, Im and I think I've always wanted to have my business time. I started my instant technology and science, and so I kind of always coming out ideas. I think the management consulting feels that sort of taught me to be analytical. Uh, understand how different sort of functions in a business and education work, understand investor interest of incentives on. But I did a lot of reading to make up for what I didn't learn in my undergrad in my grad school, um, and tried to find mentors to help me again. Sort of deal with my gaps and weaknesses. And then, uh And then I started my first company. And then the rest is is history. So because that is the question.

the world's fastest python data science and machine learning platform. So for context, data science and machine learning models could take a long time to run. If you have 10 terabytes of data, uh, you can have a model lakes, maybe 60 days to run. It doesn't really work for a lot of businesses, but none of the reality that they've been in. And historically what they've done is they've just waited. Or instead of waiting, they can down sample the data. So instead of taking the true nature and size of the data, they take a small. Fortunately, that obviously has implications for accuracy, which will be worse, and insights sort of the trust in the data quality that they later. So with that in cloud, because it's so fast, they're able to take that full volume of data, uh, Dr Analytics from it, and then make recommendations that they're a lot more confident. And we see that used in any, you know, area from AI being used in autonomous vehicles. We see it used in biomedical for sale tissue cell tissue analysis, financial services, trying to understand sort of movements in the market, three applications air really broad to big data is a proper affecting, um, everyone in the business world and research world

have an idea of what's going on, and the truth is you really have no idea what's going on. So, um, we thought that people wanted a science environment in the cloud with scalable resource is, um, that hypothesis evolved significantly over the next few months. First two weeks. Eso There's myself, sort of business founder and CEO and my co founder who's the CTO. There's also work in data science. Um, he was doing a lot of sort of product development work. I was doing a lot of customer discovery research as well as speaking with investors around different types of business models that we use. And so it was very research intensive, um, where we didn't want to strictly use our own vision and our own biases and views on what the business should be but understand from the market. Where was their demand? Where was their revenue opportunity also understand from investors? Would have financial business looked like what was a business that would be successful in that they've seen in business models that were sort of applicable to what we're doing, and it could maximize their chances of success. Um, and then we spoke with a lot of top leaders, you know, asking them, like, where should data science machinery be in the next five or 10 years? Can we go through tools? So there's a lot of research, and then you get to building a sort of initial version of the product, which you demo to customers, and you get feedback very quickly on Are they going to use this or not? And then you react to hot and cold and keep evolving on that exactly.