Apr
06
2021
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Aporia raises $5M for its AI observability platform

Machine learning (ML) models are only as good as the data you feed them. That’s true during training, but also once a model is put in production. In the real world, the data itself can change as new events occur and even small changes to how databases and APIs report and store data could have implications on how the models react. Since ML models will simply give you wrong predictions and not throw an error, it’s imperative that businesses monitor their data pipelines for these systems.

That’s where tools like Aporia come in. The Tel Aviv-based company today announced that it has raised a $5 million seed round for its monitoring platform for ML models. The investors are Vertex Ventures and TLV Partners.

Image Credits: Aporia

Aporia co-founder and CEO Liran Hason, after five years with the Israel Defense Forces, previously worked on the data science team at Adallom, a security company that was acquired by Microsoft in 2015. After the sale, he joined venture firm Vertex Ventures before starting Aporia in late 2019. But it was during his time at Adallom where he first encountered the problems that Aporio is now trying to solve.

“I was responsible for the production architecture of the machine learning models,” he said of his time at the company. “So that’s actually where, for the first time, I got to experience the challenges of getting models to production and all the surprises that you get there.”

The idea behind Aporia, Hason explained, is to make it easier for enterprises to implement machine learning models and leverage the power of AI in a responsible manner.

“AI is a super powerful technology,” he said. “But unlike traditional software, it highly relies on the data. Another unique characteristic of AI, which is very interesting, is that when it fails, it fails silently. You get no exceptions, no errors. That becomes really, really tricky, especially when getting to production, because in training, the data scientists have full control of the data.”

But as Hason noted, a production system may depend on data from a third-party vendor and that vendor may one day change the data schema without telling anybody about it. At that point, a model — say for predicting whether a bank’s customer may default on a loan — can’t be trusted anymore, but it may take weeks or months before anybody notices.

Aporia constantly tracks the statistical behavior of the incoming data and when that drifts too far away from the training set, it will alert its users.

One thing that makes Aporia unique is that it gives its users an almost IFTTT or Zapier-like graphical tool for setting up the logic of these monitors. It comes pre-configured with more than 50 combinations of monitors and provides full visibility in how they work behind the scenes. That, in turn, allows businesses to fine-tune the behavior of these monitors for their own specific business case and model.

Initially, the team thought it could build generic monitoring solutions. But the team realized that this wouldn’t only be a very complex undertaking, but that the data scientists who build the models also know exactly how those models should work and what they need from a monitoring solution.

“Monitoring production workloads is a well-established software engineering practice, and it’s past time for machine learning to be monitored at the same level,” said Rona Segev, founding partner at  TLV Partners. “Aporia‘s team has strong production-engineering experience, which makes their solution stand out as simple, secure and robust.”

 

Mar
10
2021
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Aqua Security raises $135M at a $1B valuation for its cloud native security platform

Aqua Security, a Boston- and Tel Aviv-based security startup that focuses squarely on securing cloud-native services, today announced that it has raised a $135 million Series E funding round at a $1 billion valuation. The round was led by ION Crossover Partners. Existing investors M12 Ventures, Lightspeed Venture Partners, Insight Partners, TLV Partners, Greenspring Associates and Acrew Capital also participated. In total, Aqua Security has now raised $265 million since it was founded in 2015.

The company was one of the earliest to focus on securing container deployments. And while many of its competitors were acquired over the years, Aqua remains independent and is now likely on a path to an IPO. When it launched, the industry focus was still very much on Docker and Docker containers. To the detriment of Docker, that quickly shifted to Kubernetes, which is now the de facto standard. But enterprises are also now looking at serverless and other new technologies on top of this new stack.

“Enterprises that five years ago were experimenting with different types of technologies are now facing a completely different technology stack, a completely different ecosystem and a completely new set of security requirements,” Aqua CEO Dror Davidoff told me. And with these new security requirements came a plethora of startups, all focusing on specific parts of the stack.

Image Credits: Aqua Security

What set Aqua apart, Dror argues, is that it managed to 1) become the best solution for container security and 2) realized that to succeed in the long run, it had to become a platform that would secure the entire cloud-native environment. About two years ago, the company made this switch from a product to a platform, as Davidoff describes it.

“There was a spree of acquisitions by CheckPoint and Palo Alto [Networks] and Trend [Micro],” Davidoff said. “They all started to acquire pieces and tried to build a more complete offering. The big advantage for Aqua was that we had everything natively built on one platform. […] Five years later, everyone is talking about cloud-native security. No one says ‘container security’ or ‘serverless security’ anymore. And Aqua is practically the broadest cloud-native security [platform].”

One interesting aspect of Aqua’s strategy is that it continues to bet on open source, too. Trivy, its open-source vulnerability scanner, is the default scanner for GitLab’s Harbor Registry and the CNCF’s Artifact Hub, for example.

“We are probably the best security open-source player there is because not only do we secure from vulnerable open source, we are also very active in the open-source community,” Davidoff said (with maybe a bit of hyperbole). “We provide tools to the community that are open source. To keep evolving, we have a whole open-source team. It’s part of the philosophy here that we want to be part of the community and it really helps us to understand it better and provide the right tools.”

In 2020, Aqua, which mostly focuses on mid-size and larger companies, doubled the number of paying customers and it now has more than half a dozen customers with an ARR of over $1 million each.

Davidoff tells me the company wasn’t actively looking for new funding. Its last funding round came together only a year ago, after all. But the team decided that it wanted to be able to double down on its current strategy and raise sooner than originally planned. ION had been interested in working with Aqua for a while, Davidoff told me, and while the company received other offers, the team decided to go ahead with ION as the lead investor (with all of Aqua’s existing investors also participating in this round).

“We want to grow from a product perspective, we want to grow from a go-to-market [perspective] and expand our geographical coverage — and we also want to be a little more acquisitive. That’s another direction we’re looking at because now we have the platform that allows us to do that. […] I feel we can take the company to great heights. That’s the plan. The market opportunity allows us to dream big.”

 

Jan
26
2021
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Run:AI raises $30M Series B for its AI compute platform

Run:AI, a Tel Aviv-based company that helps businesses orchestrate and optimize their AI compute infrastructure, today announced that it has raised a $30 million Series B round. The new round was led by Insight Partners, with participation from existing investors TLV Partners and S Capital. This brings the company’s total funding to date to $43 million.

At the core of Run:AI’s platform is the ability to effectively virtualize and orchestrate AI workloads on top of its Kubernetes-based scheduler. Traditionally, it was always hard to virtualize GPUs, so even as demand for training AI models has increased, a lot of the physical GPUs often set idle for long periods because it was hard to dynamically allocate them between projects.

Image Credits: Run.AI

The promise behind Run:AI’s platform is that it allows its users to abstract away all of the AI infrastructure and pool all of their GPU resources — no matter whether in the cloud or on-premises. This also makes it easier for businesses to share these resources between users and teams. In the process, IT teams also get better insights into how their compute resources are being used.

“Every enterprise is either already rearchitecting themselves to be built around learning systems powered by AI, or they should be,” said Lonne Jaffe, managing director at Insight Partners and now a board member at Run:AI.” Just as virtualization and then container technology transformed CPU-based workloads over the last decades, Run:AI is bringing orchestration and virtualization technology to AI chipsets such as GPUs, dramatically accelerating both AI training and inference. The system also future-proofs deep learning workloads, allowing them to inherit the power of the latest hardware with less rework. In Run:AI, we’ve found disruptive technology, an experienced team and a SaaS-based market strategy that will help enterprises deploy the AI they’ll need to stay competitive.”

Run:AI says that it is currently working with customers in a wide variety of industries, including automotive, finance, defense, manufacturing and healthcare. These customers, the company says, are seeing their GPU utilization increase from 25 to 75% on average.

“The new funds enable Run:AI to grow the company in two important areas: first, to triple the size of our development team this year,” the company’s CEO Omri Geller told me. “We have an aggressive roadmap for building out the truly innovative parts of our product vision — particularly around virtualizing AI workloads — a bigger team will help speed up development in this area. Second, a round this size enables us to quickly expand sales and marketing to additional industries and markets.”

Dec
09
2020
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Firebolt raises $37M to take on Snowflake, Amazon and Google with a new approach to data warehousing

For many organizations, the shift to cloud computing has played out more realistically as a shift to hybrid architectures, where a company’s data is just as likely to reside in one of a number of clouds as it might in an on-premise deployment, in a data warehouse or in a data lake. Today, a startup that has built a more comprehensive way to assess, analyse and use that data is announcing funding as it looks to take on Snowflake, Amazon, Google and others in the area of enterprise data analytics.

Firebolt, which has redesigned the concept of a data warehouse to work more efficiently and at a lower cost, is today announcing that it has raised $37 million from Zeev Ventures, TLV Partners, Bessemer Venture Partners and Angular Ventures. It plans to use the funding to continue developing its product and bring on more customers.

The company is officially “launching” today but — as is the case with so many enterprise startups these days operating in stealth — it has been around for two years already building its platform and signing commercial deals. It now has some 12 large enterprise customers and is “really busy” with new business, said CEO Eldad Farkash in an interview.

The funding may sound like a large amount for a company that has not really been out in the open, but part of the reason is because of the track record of the founders. Farkash was one of the founders of Sisense, the successful business intelligence startup, and he has co-founded Firebolt with two others who were on Sisense’s founding team, Saar Bitner as COO and Ariel Yaroshevich as CTO.

At Sisense, these three were coming up against an issue: When you are dealing in terabytes of data, cloud data warehouses were straining to deliver good performance to power its analytics and other tools, and the only way to potentially continue to mitigate that was by piling on more cloud capacity.

Farkash is something of a technical savant and said that he decided to move on and build Firebolt to see if he could tackle this, which he described as a new, difficult and “meaningful” problem. “The only thing I know how to do is build startups,” he joked.

In his opinion, while data warehousing has been a big breakthrough in how to handle the mass of data that companies now amass and want to use better, it has started to feel like a dated solution.

“Data warehouses are solving yesterday’s problem, which was, ‘How do I migrate to the cloud and deal with scale?’ ” he said, citing Google’s BigQuery, Amazon’s RedShift and Snowflake as fitting answers for that issue. “We see Firebolt as the new entrant in that space, with a new take on design on technology. We change the discussion from one of scale to one of speed and efficiency.”

The startup claims that its performance is up to 182 times faster than that of other data warehouses. It’s a SQL-based system that works on principles that Farkash said came out of academic research that had yet to be applied anywhere, around how to handle data in a lighter way, using new techniques in compression and how data is parsed. Data lakes in turn can be connected with a wider data ecosystem, and what it translates to is a much smaller requirement for cloud capacity.

This is not just a problem at Sisense. With enterprise data continuing to grow exponentially, cloud analytics is growing with it, and is estimated by 2025 to be a $65 billion market, Firebolt estimates.

Still, Farkash said the Firebolt concept was initially a challenging sell even to the engineers that it eventually hired to build out the business: It required building completely new warehouses from the ground up to run the platform, five of which exist today and will be augmented with more, on the back of this funding, he said.

And it should be pointed out that its competitors are not exactly sitting still either. Just yesterday, Dataform announced that it had been acquired by Google to help it build out and run better performance at BigQuery.

“Firebolt created a SaaS product that changes the analytics experience over big data sets,” Oren Zeev of Zeev Ventures said in a statement. “The pace of innovation in the big data space has lagged the explosion in data growth rendering most data warehousing solutions too slow, too expensive, or too complex to scale. Firebolt takes cloud data warehousing to the next level by offering the world’s most powerful analytical engine. This means companies can now analyze multi Terabyte / Petabyte data sets easily at significantly lower costs and provide a truly interactive user experience to their employees, customers or anyone who needs to access the data.”

Jul
30
2020
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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

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