AI startup Abacus.ai nabs $22 million in Series B funding to automate creation of deep learning models – ZDNet

AI startup Abacus.ai nabs $ 22 million in Series B funding to automate creation of deep learning models  ZDNet
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Abacus.AI raises another $22M and launches new AI modules

AI startup RealityEngines.AI changed its name to Abacus.AI in July. At the same time, it announced a $ 13 million Series A round. Today, only a few months later, it is not changing its name again, but it is announcing a $ 22 million Series B round, led by Coatue, with Decibel Ventures and Index Partners participating as well. With this, the company, which was co-founded by former AWS and Google exec Bindu Reddy, has now raised a total of $ 40.3 million.

Abacus co-founder Bindu Reddy, Arvind Sundararajan and Siddartha Naidu. Image Credits: Abacus.AI

In addition to the new funding, Abacus.AI is also launching a new product today, which it calls Abacus.AI Deconstructed. Originally, the idea behind RealityEngines/Abacus.AI was to provide its users with a platform that would simplify building AI models by using AI to automatically train and optimize them. That hasn’t changed, but as it turns out, a lot of (potential) customers had already invested into their own workflows for building and training deep learning models but were looking for help in putting them into production and managing them throughout their lifecycle.

“One of the big pain points [businesses] had was, ‘look, I have data scientists and I have my models that I’ve built in-house. My data scientists have built them on laptops, but I don’t know how to push them to production. I don’t know how to maintain and keep models in production.’ I think pretty much every startup now is thinking of that problem,” Reddy said.

Image Credits: Abacus.AI

Since Abacus.AI had already built those tools anyway, the company decided to now also break its service down into three parts that users can adapt without relying on the full platform. That means you can now bring your model to the service and have the company host and monitor the model for you, for example. The service will manage the model in production and, for example, monitor for model drift.

Another area Abacus.AI has long focused on is model explainability and de-biasing, so it’s making that available as a module as well, as well as its real-time machine learning feature store that helps organizations create, store and share their machine learning features and deploy them into production.

As for the funding, Reddy tells me the company didn’t really have to raise a new round at this point. After the company announced its first round earlier this year, there was quite a lot of interest from others to also invest. “So we decided that we may as well raise the next round because we were seeing adoption, we felt we were ready product-wise. But we didn’t have a large enough sales team. And raising a little early made sense to build up the sales team,” she said.

Reddy also stressed that unlike some of the company’s competitors, Abacus.AI is trying to build a full-stack self-service solution that can essentially compete with the offerings of the big cloud vendors. That — and the engineering talent to build it — doesn’t come cheap.

Image Credits: Abacus.AI

It’s no surprise then that Abacus.AI plans to use the new funding to increase its R&D team, but it will also increase its go-to-market team from two to ten in the coming months. While the company is betting on a self-service model — and is seeing good traction with small- and medium-sized companies — you still need a sales team to work with large enterprises.

Come January, the company also plans to launch support for more languages and more machine vision use cases.

“We are proud to be leading the Series B investment in Abacus.AI, because we think that Abacus.AI’s unique cloud service now makes state-of-the-art AI easily accessible for organizations of all sizes, including start-ups. Abacus.AI’s end-to-end autonomous AI service powered by their Neural Architecture Search invention helps organizations with no ML expertise easily deploy deep learning systems in production.”

 

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RealityEngines.AI becomes Abacus.AI and raises $13M Series A

RealityEngines.AI, the machine learning startup co-founded by former AWS and Google exec Bindu Reddy, today announced that it is rebranding as Abacus.AI and launching its autonomous AI service into general availability.

In addition, the company also today disclosed that it has raised a $ 13 million Series A round led by Index Ventures’ Mike Volpi, who will also join the company’s board. Seed investors Eric Schmidt, Jerry Yang and Ram Shriram also participated in this oversubscribed round, with Shriram also joining the company’s board. New investors include Mariam Naficy, Erica Shultz, Neha Narkhede, Xuezhao Lan and Jeannette Furstenberg. 

This new round brings the company’s total funding to $ 18.25 million.

Abacus.AI’s co-founders Bindu Reddy, Arvind Sundararajan and Siddartha Naidu (Image Credits: Abacus.AI)

At its core, RealityEngines.AI’s Abacus.AI’s mission is to help businesses implement modern deep learning systems into their customer experience and business processes without having to do the heavy lifting of learning how to train models themselves. Instead, Abacus takes care of the data pipelines and model training for them.

The company worked with 1,200 beta testers and in recent months, the team mostly focused on not just helping businesses build their models but also put them into production. Current Abacus.AI customers include 1-800-Flowers, Flex, DailyLook and Prodege.

“My guess would be that out of the hundred projects which are started in ML, one percent succeeds because of so many moving parts,” Reddy told me. “You have to build the model, then you have to test it in production — and then you have to build data pipelines and have to put in training pipelines. So over the last few weeks even, we’ve added a whole bunch of features to enable putting these things to go into production more smoothly — and we continue to add to it.”

Image Credits: Abacus.AI

In recent months, the team also added new unsupervised learning tools to its lineup of pre-built solutions to help users build systems for anomaly detection around transaction fraud and account takeovers, for example.

The company also today released new tools for debiasing data sets that can be used on already trained algorithms. Automatically building training sets — even with relatively small data sets — is one of the areas on which the Abacus team has long focused, and it is now using some of these same techniques to tackle this problem. In its experiments, the company’s facial recognition algorithm was able to greatly improve its ability to detect whether a Black celebrity was smiling or not, for example, even though the training data set featured 22 times more white people.

Image Credits: Abacus

With today’s launch, Abacus is also launching a new section on its website to showcase models from its community. “You can go build a model, tweak your model if you want, use your own data sets — and then you can actually share the model with the community,” Reddy explained, and noted that this is now possible because of Abacus’ new pricing model. The company has decided to only charge customers when they put models into production.

Image Credits: Abacus.ai

The next major item on the Abacus roadmap is to build more connectors to third-party systems so that users can easily import data from Salesforce and Segment, for example. In addition, Reddy notes that the team will build out more of its pre-built solutions, including more work on language understanding and vision use cases.

To do this, Abacus has already hired more research scientists to work on some of its fundamental research projects, something Reddy says its funders are quite comfortable with, and more engineers to put that work into practice. She expects the team will grow from 22 employees today to about 35 by the end of the year.

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