Bloomberg

“I think that the business of selling data is, is a very difficult business, and from a Bloomberg perspective, we've, you know, spoken, or we speak very often to startups that come to us with the hope to sell their data, raw data, and I think they quite frequen…”

“Twitter has become kind of the quickest way to find out about, ah, breaking news outside of Bloomberg.”

“Fundamentally, Bloomberg was the first big data company out there, I think.”

“The Bloomberg infrastructure handles, every day, forty-five billion ticks, just to give you a sense of, you know, when we're talking about big data.”

“10 petabytes of storage deployed in our data centers.”

“The most important data which is the, Transactional and conversational messaging that goes on inside the platform is created natively for the platform and stays inside the platform. And I think that's the glue that keeps everybody on Bloomberg.”

“More recently they bought BNA, which is a company that was founded in two, in 1929. For about a billion dollars.”

“Bloomberg tried to do a lot of the things that we were doing and did not succeed. Lexus has tried to do a lot of these things and has not succeeded. It's not something where you just throw resources at it.”

“Bloomberg, ah, as you guys know, is, ah, pretty much the first big data company that ever existed long, long before the term big data was coined.”

“We also sell this as a data feed which is an interesting kind of emerging model for our business is how do we provide data not just to people but also to machines.”

“Our business is we sell a data product like, you know, like the way Bloomberg does and not, not at quite the price point Bloomberg does, but, you know, our average ticket I think is around 35 K a year.”

“My personal view is, you know, 10 years from now, where it's going is clear, and I think that, when I started the company, companies like Bloomberg, for example, would say, no way, no how, we'll never do anything like, you know, like, you know, big data in the…”

“It's a collaboration of Jupyter, Bloomberg, and Continuum”

“So at Bloomberg, we built this library called bqplot, which is an interactive plotting library for Jupyter.”

“And we want to get to a place where you can search all that in one screen, and we will give you that information in one single ranked list, so you don't have to learn more and more different places to search for data.”

“One was, we were looking for something that was truly open source, where we had a lot more control over open source and, sorry, source code, and a lot more saying how the direction goes.”

“We had a huge improvement once we started using it, but more than that improvement, the good part about learning to rank is it always continues to improve your model without constant need of people tweaking it.”

“We use unsupervised learning a lot to Sort of bootstrap our data. For example, word to whack, right? It's a perfect unsupervised learning algorithm. We use that a lot to, for query reformulation and all that, but, ah, it's a little risky for something as high …”

“So that's what we use for, ah, we use to decide which model to push out.”

“So a lot of the luxury that other companies have, like doing large scale machine learning based on massive amount of usage data, or doing large scale A-B testing to decide which model works better, doesn't work for us.”

“The one we use is called Lambda Mart, which is based on decision trees, or gradient, regression trees, actually.”

“I think about the last 15, 15 or 16 versions of Solr have had some sort of our code in it”

“Our manually generated stories, which are frankly much more informative”

“And they feed over a hundred different screens within the Bloomberg terminal, which provide company trend information, company tweet information, sentiment information, sentiment alerts, tweets on mergers, tweets by the president of the United States, et ceter…”

“And this kind of article we can now automatically generate. So you can get this with no human intervention within two minutes, and then within five minutes a human journalist can come along in art color.”

“So the Bloomberg sentiment feed is not just positive or negative. I like it right. It's buy or sell, and it's with probability so that you can use those in your own models.”

“The distance between 10 38 and 10 39 Was about three percent in the stock price. The distance between the SEC headline and 1055, 20 minutes later, was a 12% absolute difference in the stock price.”

“And that's something that historically has been done mostly with rule-based systems, but today we do it with deep learning and a lot of decision trees.”

“We do a lot of historical monitoring, so we can know how old a social media account is. We also have a very, very large collection of pieces of information about entities in the world, so we can know how closely associated a particular handle is. With a decisi…”

“At Bloomberg, we're not trying to identify net promoter score. We're trying to really understand what's going on so that someone else can decide whether they should buy or sell this company.”

“There are, nobody has told me the actual number. I've heard numbers from between 15 and 40,000 individual functions inside the Bloomberg. Each of them is fed by at least one data feed, and in many cases, several.”

“When I look at the, you know, all of the LLM technology, large language model technology, it seems like it will get closer than any of the things that, that, you know, the field has been doing for the past few decades.”

“We have over 2000 companies using the enterprise hub today from kind of like Mercedes, Bloomberg all the way to larger ones like Nvidia, for example, that I was mentioning.”