The Exchanges

Q And you mentioned examples, um, with videos, which, uh, qualifies as well as document and sounds that qualifies as unstructured data. Uh, but, but, uh, deployment can be used on, on tabular data as well, right? What, what are some examples and some use cases for tabular data?

A Yeah, I mean, that's my favorite topic. If you guys look at Abacus, you will see that our first focus was all on tabular data. And the reason we focused on tabular data and why tabular data is actually pretty effective with deep learning is, uh, uh, one, uh, because every company has multiple different, uh, uh, examples or machine learning, uh, models that they want to develop using tabular data. At its most basic level, we have something called predictive modeling. Predictive modeling is very similar to Finding a variable, uh, which is what we, what we call a dependent variable based on other independent variables. Let me give you a concrete use case. If you go and look for a home on Zillow or Redfin, you have something called the house property price, right? Estimate, the Z estimate. I'm sure everyone's seen this. The Z estimate comes because they have a machine learning model, which is a predictive model, which is using all kinds of properties, like say bedrooms, bathrooms, square footage, neighborhood, location, and whatnot. And trying to determine the price of the house based on recent homes, which have sold, uh, you know, with those attributes. So it's learning constantly based on recent sales, and then trying to predict based on the attributes of your home, what the price would be, right? That's a predictive model. And these predictive models, there are very many methods…

AI assessment note: “If you go and look for a home on Zillow or Redfin, you have something”

Q Great. Who's an ideal customer for the platform? Uh, is that a, like a fortune 1000 company that, uh, doesn't have any general resources that it started? Like who's the ideal customer?

A So we have a bunch of, I would say, startups and medium-sized companies. So we have, I would say, two classes of ideal customers. One is the startup of the medium-sized company who basically wants to go, go, go, right? They have like seven different ML use cases. They may have anywhere between two to five data scientists, sometimes zero. So let's say zero to five data scientists, and they want to get things in production. What we've seen all the time is, I'll give you a classic example of this. This is a medium-sized company, not really a startup. It's a company called USIC LLC. I'm picking this one because they're the eight one one company. When you call them, you call them every time you want to dig somewhere because they have to come and mark exactly where a particular, you know, utility is so that you don't dig, dig it and like, you know, harm the utility. They have, they're a pretty large company. I think they're about 500 plus employees. They have a ton of data. They want to be able to like build models which are like, Probability of a particular site getting damaged. Time to, like, go and, you know, make that inspection. So they have multiple different machine learning models, and they have a lot of scale, and, you know, they would rather use Abacus than not. So that would be our, like, you know, I would say a sweet spot customer in a small to mid-sized company. Then we …

AI assessment note: “So we have, I would say, two classes of ideal customers.”

Q learning model. Your data is your leverage and your best most against competition. Don't wait to hire a data science team. Use a low-code ML platform and get started ASAP. Um, so you've, you've seen people do this, um, successfully or what, what, how does one, uh, get started? I guess what, what's, what are some examples of like low-code ML platforms and how do people go about doing that?

A Well, it's a, it's a self-promotional tweet. So an example of a low-code ML platform would be Abacus. Um, I mean, I'm sure there are others as well. Uh, but, um, you know, I'll give you an example. One of our customers, a startup called Daily Look, uh, you know, they're kind of like Stitch Fix, uh, except their, their business model is slightly different in the sense that, uh, I think you, you get to choose certain things that you want in your box. But presumably every month you get a box of clothing that you might like. You keep some, you, you, you, you, you know, you return some. So, you know, even when they were pretty small, they had a few thousand customers, and they didn't have a machine learning team, they started using us. And they've been using us for a year. They're one of our oldest customers. And, you know, now they're up to model three. And, you know, it's worked really well for them because they kind of knew what the problem was. They wanted to be able to optimize the amount of, Uh, uh, you know, the number of things that you kept every month, clearly, right? So you have to pick the right, uh, you know, style items to send them on a monthly basis. Clearly that's a machine learning problem. You don't have to wait until you have millions of data points. Even hundreds of thousands is too much. If you have 10,000 data points, that's my rule of thumb. If you have 10,00…

AI assessment note: “An example of a low-code ML platform would be Abacus.”

Q Great question that is going to enable you to like dish out on the competitors. What are some of your competitive advantages or even drawbacks compared to H two other AI and data robot?

A Yeah. So if somebody actually knows our space, but HTO and data robot are really, we, I mean, maybe they consider themselves our competitors. We like to think of them as non-competitors. The reason for that is their real focus, as you probably may know, is on the auto ML or the automated machine learning part of it. Our focus really is to do that end to end. We've spent a ton of time in the data wrangling data management, you know, and doing that across multiple sources. So literally you can connect, you know, your data from Snowflake, from Redshift, and from your data lake and Salesforce together and bring that all together. So I think our biggest competitive advantage is that data wrangling and the real time feature store, which is part of our service.

AI assessment note: “our biggest competitive advantage is that data wrangling and the real time feature store”

Q All right. I'd love to like switch to some questions from, uh, the, the, the group. We have some good ones. Um, uh, let's see, like one from Gedalia, who I think may have asked a question before this as well. What is the state of the art around labeling data? We really struggle with obtaining enough labeled training data.

A Oh, it's a very good question. Uh, I mean, label data is really difficult. There's a whole company called Scale AI, which is, I think there's a bunch of companies around Scale AI too, which help you with labeling data. Uh, the good news is there are more and more models, and if you wait for two years, there'll be even more, but right now even there are lots and lots of new models which are coming up, which don't require too much labeling. So take the best, most thought, talked about one in the world, GPT-III. The whole reason GPT-III is the most popular language model that is in the world, Today are talked about most in the world is because you need very little label data. So as an example, you can go tell, ah, you can give GPT- three five examples, and then it'll start learning very quickly. So, ah, you know, I think the state of the art is moving very rapidly, ah, and so that you can, like, teach models with less and less data, and this will be the case for both images and language very soon.

AI assessment note: “the state of the art is moving very rapidly, ah, and so that you can, like, teach models with less and less data”

Q Very good. And, um, what, what are you, uh, building next? What, what is the roadmap? You're, you're still a young startup. So I imagine like you're, you're releasing a lot of stuff at a rapid cadence. What's on the docket for this coming year?

A Sure. I mean, when, when I say what I say, a lot of people are incredulous, to be honest, because they're like, is this really possible to do? So I would say in terms of like where we are in our current product evolution on a zero to 10 scale, we're around seven, 7.5, meaning we're, you know, we're still having to add a lot of stuff because we learn as we go, as we get new customers, uh, and there's a lot of demand for our service right now. And so Uh, you know, as we get new customers, we want to make sure that the customer is actually getting what we are talking about, which is this end-to-end autonomous AI. So large part of our, you know, work is focused on making that happen. Uh, things like, you know, making sure that data transformations are robust and so on. Having said that, the key other focus we have for this year is, uh, releasing a whole bunch of use cases beyond structured data and unsupervised learning, which we just released, uh, late last year, which was around anomaly detection. Uh, this, um, you know, next quarter, we're going to be releasing a whole bunch of language, uh, use cases, support for language and Q three, Q four is support for vision. So support for, uh, image use cases.

AI assessment note: “next quarter, we're going to be releasing a whole bunch of language, uh, use cases”