Nov
18
2020
--

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,” Yanda Erlich, a p artner at Coatue Ventures  told me. “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.”

 

Sep
23
2020
--

WhyLabs brings more transparancy to ML ops

WhyLabs, a new machine learning startup that was spun out of the Allen Institute, is coming out of stealth today. Founded by a group of former Amazon machine learning engineers, Alessya Visnjic, Sam Gracie and Andy Dang, together with Madrona Venture Group principal Maria Karaivanova, WhyLabs’ focus is on ML operations after models have been trained — not on building those models from the ground up.

The team also today announced that it has raised a $4 million seed funding round from Madrona Venture Group, Bezos Expeditions, Defy Partners and Ascend VC.

Visnjic, the company’s CEO, used to work on Amazon’s demand forecasting model.

“The team was all research scientists, and I was the only engineer who had kind of tier-one operating experience,” she told me. “So I thought, “Okay, how bad could it be? I carried the pager for the retail website before. But it was one of the first AI deployments that we’d done at Amazon at scale. The pager duty was extra fun because there were no real tools. So when things would go wrong — like we’d order way too many black socks out of the blue — it was a lot of manual effort to figure out why issues were happening.”

Image Credits: WhyLabs

But while large companies like Amazon have built their own internal tools to help their data scientists and AI practitioners operate their AI systems, most enterprises continue to struggle with this — and a lot of AI projects simply fail and never make it into production. “We believe that one of the big reasons that happens is because of the operating process that remains super manual,” Visnjic said. “So at WhyLabs, we’re building the tools to address that — specifically to monitor and track data quality and alert — you can think of it as Datadog for AI applications.”

The team has brought ambitions, but to get started, it is focusing on observability. The team is building — and open-sourcing — a new tool for continuously logging what’s happening in the AI system, using a low-overhead agent. That platform-agnostic system, dubbed WhyLogs, is meant to help practitioners understand the data that moves through the AI/ML pipeline.

For a lot of businesses, Visnjic noted, the amount of data that flows through these systems is so large that it doesn’t make sense for them to keep “lots of big haystacks with possibly some needles in there for some investigation to come in the future.” So what they do instead is just discard all of this. With its data logging solution, WhyLabs aims to give these companies the tools to investigate their data and find issues right at the start of the pipeline.

Image Credits: WhyLabs

According to Karaivanova, the company doesn’t have paying customers yet, but it is working on a number of proofs of concepts. Among those users is Zulily, which is also a design partner for the company. The company is going after mid-size enterprises for the time being, but as Karaivanova noted, to hit the sweet spot for the company, a customer needs to have an established data science team with 10 to 15 ML practitioners. While the team is still figuring out its pricing model, it’ll likely be a volume-based approach, Karaivanova said.

“We love to invest in great founding teams who have built solutions at scale inside cutting-edge companies, who can then bring products to the broader market at the right time. The WhyLabs team are practitioners building for practitioners. They have intimate, first-hand knowledge of the challenges facing AI builders from their years at Amazon and are putting that experience and insight to work for their customers,” said Tim Porter, managing director at Madrona. “We couldn’t be more excited to invest in WhyLabs and partner with them to bring cross-platform model reliability and observability to this exploding category of MLOps.”

Jul
30
2020
--

Buildots raises $16M to bring computer vision to construction management

Buildots, a Tel Aviv and London-based startup that is using computer vision to modernize the construction management industry, today announced that it has raised $16 million in total funding. This includes a $3 million seed round that was previously unreported and a $13 million Series A round, both led by TLV Partners. Other investors include Innogy Ventures, Tidhar Construction Group, Ziv Aviram (co-founder of Mobileye & OrCam), Magma Ventures head Zvika Limon, serial entrepreneurs Benny Schnaider and  Avigdor Willenz, as well as Tidhar chairman Gil Geva.

The idea behind Buildots is pretty straightforward. The team is using hardhat-mounted 360-degree cameras to allow project managers at construction sites to get an overview of the state of a project and whether it remains on schedule. The company’s software creates a digital twin of the construction site, using the architectural plans and schedule as its basis, and then uses computer vision to compare what the plans say to the reality that its tools are seeing. With this, Buildots can immediately detect when there’s a power outlet missing in a room or whether there’s a sink that still needs to be installed in a kitchen, for example.

“Buildots have been able to solve a challenge that for many seemed unconquerable, delivering huge potential for changing the way we complete our projects,” said Tidhar’s Geva in a statement. “The combination of an ambitious vision, great team and strong execution abilities quickly led us from being a customer to joining as an investor to take part in their journey.”

The company was co-founded in 2018 by Roy Danon, Aviv Leibovici and Yakir Sundry. Like so many Israeli startups, the founders met during their time in the Israeli Defense Forces, where they graduated from the Talpiot unit.

“At some point, like many of our friends, we had the urge to do something together — to build a company, to start something from scratch,” said Danon, the company’s CEO. “For us, we like getting our hands dirty. We saw most of our friends going into the most standard industries like cloud and cyber and storage and things that obviously people like us feel more comfortable in, but for some reason we had like a bug that said, ‘we want to do something that is a bit harder, that has a bigger impact on the world.’ ”

So the team started looking into how it could bring technology to traditional industries like agriculture, finance and medicine, but then settled upon construction thanks to a chance meeting with a construction company. For the first six months, the team mostly did research in both Israel and London to understand where it could provide value.

Danon argues that the construction industry is essentially a manufacturing industry, but with very outdated control and process management systems that still often relies on Excel to track progress.

Image Credits: Buildots

Construction sites obviously pose their own problems. There’s often no Wi-Fi, for example, so contractors generally still have to upload their videos manually to Buildots’ servers. They are also three dimensional, so the team had to develop systems to understand on what floor a video was taken, for example, and for large indoor spaces, GPS won’t work either.

The teams tells me that before the COVID-19 lockdowns, it was mostly focused on Israel and the U.K., but the pandemic actually accelerated its push into other geographies. It just started work on a large project in Poland and is scheduled to work on another one in Japan next month.

Because the construction industry is very project-driven, sales often start with getting one project manager on board. That project manager also usually owns the budget for the project, so they can often also sign the check, Danon noted. And once that works out, then the general contractor often wants to talk to the company about a larger enterprise deal.

As for the funding, the company’s Series A round came together just before the lockdowns started. The company managed to bring together an interesting mix of investors from both the construction and technology industries.

Now, the plan is to scale the company, which currently has 35 employees, and figure out even more ways to use the data the service collects and make it useful for its users. “We have a long journey to turn all the data we have into supporting all the workflows on a construction site,” said Danon. “There are so many more things to do and so many more roles to support.”

Image Credits: Buildots

Powered by WordPress | Theme: Aeros 2.0 by TheBuckmaker.com