Dataiku

“You had a need for the enterprise to do more in terms of data science, An ML, but a scale gap, because there was not enough data scientists on planet Earth in order to fill that gap.”

“It should not be solved by adding more data scientists, because anyway, there's not enough data scientists, and if they don't understand the business, there's no point. It should be solved by finding a way for people from the business that are very much into d…”

“A thing that it required then some will or independence of mind, which was easier for us because I'm not sure if I have had the will or the mind power to Keep this line of thinking if I had been in the Bay Area. Because around me, everyone would have had a dif…”

“But our perspective was that, how do we solve for the problem of every other companies, which will not follow the same route, which cannot follow the same route, and take the perspective of, like, we will never try to sell aggressively to tech companies, per s…”

“This constant integration of technologies has been part of the DNA of Dataiku. And it's uncomfortable when you do enterprise software, because it means that like every quarter or so, twice a year at least, you've got something else going on, and you're not con…”

“When you're building in data science, people tend to redo from scratch, or even analytics and so forth.”

“Our vision is that enterprise at large will not have one agent, but like more, 500 or 1000 of them.”

“All of those capabilities could be startups by themselves, but the same way we thought about the platform required for the enterprise back in the day, 10 years ago, we think that for the world of LLM, all of those capabilities will just become features of plat…”

“Then enterprise, in order to differentiate and be, transform themselves, will also need to build their own agents. On everything which is about stitching together the data from one vendor to the other, or the overall process, or the thing they actually built, …”

“I think it's at least 20. I mean, I think it's almost, it's probably very comparable to the number of years in order to get a kid in or even out of college, in and out of college, and maybe being being financially independent, you know? That's kind of like the…”

“And back then, we crossed 600 customers. We were two thirty million of IRR. Back then, and we have more than 1000 employees.”

“So we've got about 100 customers that used our platform for some LLM associated use cases.”

“Because of the evolution of technologies and LLM out there, it's very likely that you will have to switch from one provider to the other over the course of your application, if your application is successful.”

“It's actually fairly realistic for an enterprise to have hundreds Of Generative AI applications in production at some point.”

“We see quite a bit of usage patterns where people would start GPT-IV for design and then decide to move to Essentially something cheaper, like nixtral self-hosted or nixtral self-hosted when moving to production.”

“That Taiku is 10 years old as a company. And in terms of numbers, we are at north of 1200 employees and we ended up last year north of two hundred million of revenue of annual recurring revenue.”

“In many businesses applying models that are actually not so complicated, but like, still being, you know, with real life data and with a large amount of data, you outperform your previous business rule by a significant margin.”

“I think that in the next five to 10 years, it will be actually Universally mainstream, as in most companies, a very vast majority of companies will have hundreds, if not thousands of automated systems that automate ordering and some form of cash management and…”

“We are six, 50 employees and doubling kind of on a year to year basis.”

“There is no way for the enterprise in the long term, if you move forward and put yourself from a, if you go forward by 10 years, that you have like five, six, seven, eight different tools that you use to manage the data life cycle. It will make no sense.”

“Meaning a few years ago doing all those EV lifting of being able to do ML on a few gigabytes of data was possibly taking a few days to any seasoned data engineers. Today, you can actually do the same kind of achievement in a few clicks”

“In order to make it happen, you need indeed to facilitate for business users the ability to just write those rules so that when a data scientist is building a machine learning model, those rules are actually checked. That's where actually the value comes becau…”

“In, in some use cases, it's not machine learning per se, or the performance of machine learning models that is the choke point in order to deliver value. It's the ability to actually meet the, meaning the regulatory, the regulation constraints, meaning literal…”

“Literally speaking, where are the treasure troves of data and AI? Where are the people having the will to do more with data and AI? I must admit, and it will be painful, but indeed it's in the US.”

“Data, it's here. It's no longer a problem. And I think the real challenge today is about, like, and the change in data, like, the people will make it happen.”

“Every data science story I heard in the last few years actually started with someone.”

“Data science is actually a mix of business and tech, and the challenge is actually to make business people and tech people get on board in data science together and work collaboratively on project, because you cannot really deliver data science without having …”

“When you want to build software, well, you need to listen to your customers, but you also need to have strong beliefs and try to implement them in your software like a maniac.”

“First part of our credo is that, well, you should kind of separate specification and execution as much as possible in data.”

“So the one that we've got here in the U.S., it's primarily a Cloudera Hadoop stack that we've used a blueprint that was essentially blessed by our brethren over in French, in France and with that, we've got R and Python and Spark. We use some Dataiku along the…”

“I didn't really make the stats, but I think the main reason for a big data project to fail is not having the data.”

“When you do have the data, and you do have the real business problem you want to solve, it's like you already did, like, half of the job, and so maybe you spend, like, more, 30 more percent actually working on the domain-specific or problem-specific features y…”

“I consider my main competitor to be the SAS, SAS Institute, meaning, ah, actually that's the company I want to replace, because I don't like SAS.”

“AXA, AXA is our customer for about, ah, two years, I think. And so, they are using our software internally in their, ah, data initiative. So yeah, we have about 30 seats of, at AXA, 30 users at AXA, I think.”