Feb
24
2021
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Aquarium scores $2.6M seed to refine machine learning model data

Aquarium, a startup from two former Cruise employees, wants to help companies refine their machine learning model data more easily and move the models into production faster. Today the company announced a $2.6 million seed led by Sequoia with participation from Y Combinator and a bunch of angel investors including Cruise co-founders Kyle Vogt and Dan Kan.

When the two co-founders CEO Peter Gao and head of engineering Quinn Johnson, were at Cruise they learned that finding areas of weakness in the model data was often the problem that prevented it from getting into production. Aquarium aims to solve this issue.

“Aquarium is a machine learning data management system that helps people improve model performance by improving the data that it’s trained on, which is usually the most important part of making the model work in production,” Gao told me.

He says that they are seeing a lot of different models being built across a variety of industries, but teams are getting stuck because iterating on the data set and continually finding relevant data is a hard problem to solve. That’s why Aquarium’s founders decided to focus on this.

“It turns out that most of the improvement to your model, and most of the work that it takes to get it into production is about deciding, ‘Here’s what I need to go and collect next. Here’s what I need to go label. Here’s what I need to go and retrain my model on and analyze it for errors and repeat that iteration cycle,” Gao explained.

The idea is to get a model into production that outperforms humans. One customer Sterblue offers a good example. They provide drone inspection services for wind turbines. Their customers used to send out humans to inspect the turbines for damage, but with a set of drone data, they were able to train a machine learning model to find issues. Using Aquarium, they refined their model and improved accuracy by 13%, while cutting the cost of human reviews in half, Gao said.

The 7 person Aquarium startup team.

The Aquarium team. Image: Aquarium

Aquarium currently has 7 employees including the founders, of which three are women. Gao says that they are being diverse by design. He understands the issues of bias inherent in machine learning model creation, and creating a diverse team for this kind of tooling is one way to help mitigate that bias.

The company launched last February and spent part of the year participating in the Y Combinator Summer 2020 cohort. They worked on refining the product throughout 2020, and recently opened it up from beta to generally available.

Feb
17
2021
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TigerGraph raises $105M Series C for its enterprise graph database

TigerGraph, a well-funded enterprise startup that provides a graph database and analytics platform, today announced that it has raised a $105 million Series C funding round. The round was led by Tiger Global and brings the company’s total funding to over $170 million.

“TigerGraph is leading the paradigm shift in connecting and analyzing data via scalable and native graph technology with pre-connected entities versus the traditional way of joining large tables with rows and columns,” said TigerGraph founder and CEO, Yu Xu. “This funding will allow us to expand our offering and bring it to many more markets, enabling more customers to realize the benefits of graph analytics and AI.”

Current TigerGraph customers include the likes of Amgen, Citrix, Intuit, Jaguar Land Rover and UnitedHealth Group. Using a SQL-like query language (GSQL), these customers can use the company’s services to store and quickly query their graph databases. At the core of its offerings is the TigerGraphDB database and analytics platform, but the company also offers a hosted service, TigerGraph Cloud, with pay-as-you-go pricing, hosted either on AWS or Azure. With GraphStudio, the company also offers a graphical UI for creating data models and visually analyzing them.

The promise for the company’s database services is that they can scale to tens of terabytes of data with billions of edges. Its customers use the technology for a wide variety of use cases, including fraud detection, customer 360, IoT, AI and machine learning.

Like so many other companies in this space, TigerGraph is facing some tailwind thanks to the fact that many enterprises have accelerated their digital transformation projects during the pandemic.

“Over the last 12 months with the COVID-19 pandemic, companies have embraced digital transformation at a faster pace driving an urgent need to find new insights about their customers, products, services, and suppliers,” the company explains in today’s announcement. “Graph technology connects these domains from the relational databases, offering the opportunity to shrink development cycles for data preparation, improve data quality, identify new insights such as similarity patterns to deliver the next best action recommendation.”

Feb
17
2021
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Peak AI nabs $21M for a platform to help non-tech companies make AI-based decisions

One of the biggest challenges for organizations in modern times is deciding where, when and how to use the advances of technology, when the organizations are not technology companies themselves. Today, a startup out of Manchester, England, is announcing some funding for a platform that it believes can help.

Peak AI, which has built technology that it says can help enterprises — specifically those that work with physical products such as retailers, consumer goods companies and manufacturing organizations — make better, AI-based evaluations and decisions, has closed a round of $21 million.

The Series B is being led by Oxx, with participation from past investors MMC Ventures and Praetura Ventures, as well as new backer Arete. It has raised $43 million to date and is not disclosing its valuation.

Richard Potter, the CEO who co-founded the company with Atul Sharma and David Leitch, said that the funding will be used to continue expanding the functionality of its platform, adding offices in the U.S. and India, and growing its customer base.

Its list of clients today is an impressive one, including the retailer PrettyLittleThing, KFC, PepsiCo, Marshalls and Speedy Hire.

As Potter describes it, Peak identified its opportunity early on. It was founded in 2014, a time non-tech enterprises were just starting to grasp how the concept of AI could apply to their businesses but felt it was out of their reach.

Indeed, the larger landscape for AI services at that time was primarily one focused on technology companies, specifically companies like Google, Amazon and Apple that were building AI products to power their own services, and often snapping up the most interesting talent in the field as it manifested through smaller startups and universities.

Peak’s basic premise was to build AI not as a business goal for itself but as a business service. Its platform sits within an organization and ingests any data source that a company might wish to feed into it.

While initial integration needs technical know-how — either at the company itself or via a systems integrator — using Peak day-to-day can be done by both technical and non-technical workers.

Peak says it can help answer a variety of questions that those people might have, such as how much of an item to produce, and where to ship it, based on a complex mix of sales data; how to manage stock better; or when to ramp up or ramp down headcount in a warehouse. The platform can also be used to help companies with marketing and advertising, figuring out how to better target campaigns to the right audiences, and so on.

Peak is not the first company that has seized on the concept of using a “general” AI to give non-tech organizations the same kinds of superpowers that the likes of big tech now use in their own businesses everyday.

Sometimes the ambition has outstripped the returns, however.

Witness Element AI, a highly-touted startup backed by a long list of top-shelf strategic and financial investors to build, essentially, an AI services business for non-tech companies to use as they might these days use Accenture. It never quite got there, though, and was acquired by ServiceNow last year at a devalued price of $500 million, the customer deals it had were wound down, and the tech was integrated into the bigger company’s stack.

Other efforts within hugely successful tech companies have not fared that well either.

“Einstein’s features are essentially useless, and you can quote me on that,” said Potter of Salesforce’s in-house CRM AI business. “Because it is too generic, it doesn’t predict anything useful.”

And that is perhaps the crux of why Peak AI is working for now: it has remained focused for now on a limited number of segments of the market, in particular those with physical objects as the end product, giving the AI that it has built a more targeted end point. In other words, it’s “general” but only for specific industries.

And it claims that this is paying off. Peak’s customers have reported a 5% increase in total company revenues, a doubling of return on advertising spend, a 12% reduction in inventory holdings and a 5% reduction in supply chain costs, according to the company (although it doesn’t specify which companies, which products or anything that points to who or what is being described).

“Richard and the excellent Peak team have a compelling vision to optimize entire businesses through Decision Intelligence and they’re delivering real-world benefits to a raft of household name customers already,” said Richard Anton, a general partner at Oxx, in a statement. “The pandemic has meant digitization is no longer a choice; it’s a requirement. Peak has made it easier for businesses to get started and see rapid results from AI-enabled decision making. We are delighted to support Peak on their way to becoming the category-defining global leader in Decision Intelligence.” Anton is joining the board with this round.

Feb
11
2021
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Base Operations raises $2.2 million to modernize physical enterprise security

Typically when we talk about tech and security, the mind naturally jumps to cybersecurity. But equally important, especially for global companies with large, multinational organizations, is physical security — a key function at most medium-to-large enterprises, and yet one that to date, hasn’t really done much to take advantage of recent advances in technology. Enter Base Operations, a startup founded by risk management professional Cory Siskind in 2018. Base Operations just closed their $2.2 million seed funding round and will use the money to capitalize on its recent launch of a street-level threat mapping platform for use in supporting enterprise security operations.

The funding, led by Good Growth Capital and including investors like Magma Partners, First In Capital, Gaingels and First Round Capital founder Howard Morgan, will be used primarily for hiring, as Base Operations looks to continue its team growth after doubling its employe base this past month. It’ll also be put to use extending and improving the company’s product and growing the startup’s global footprint. I talked to Siskind about her company’s plans on the heels of this round, as well as the wider opportunity and how her company is serving the market in a novel way.

“What we do at Base Operations is help companies keep their people in operation secure with ‘Micro Intelligence,’ which is street-level threat assessments that facilitate a variety of routine security tasks in the travel security, real estate and supply chain security buckets,” Siskind explained. “Anything that the chief security officer would be in charge of, but not cyber — so anything that intersects with the physical world.”

Siskind has firsthand experience about the complexity and challenges that enter into enterprise security since she began her career working for global strategic risk consultancy firm Control Risks in Mexico City. Because of her time in the industry, she’s keenly aware of just how far physical and political security operations lag behind their cybersecurity counterparts. It’s an often overlooked aspect of corporate risk management, particularly since in the past it’s been something that most employees at North American companies only ever encounter periodically when their roles involve frequent travel. The events of the past couple of years have changed that, however.

“This was the last bastion of a company that hadn’t been optimized by a SaaS platform, basically, so there was some resistance and some allegiance to legacy players,” Siskind told me. “However, the events of 2020 sort of turned everything on its head, and companies realized that the security department, and what happens in the physical world, is not just about compliance — it’s actually a strategic advantage to invest in those sort of services, because it helps you maintain business continuity.”

The COVID-19 pandemic, increased frequency and severity of natural disasters, and global political unrest all had significant impact on businesses worldwide in 2020, and Siskind says that this has proven a watershed moment in how enterprises consider physical security in their overall risk profile and strategic planning cycles.

“[Companies] have just realized that if you don’t invest [in] how to keep your operations running smoothly in the face of rising catastrophic events, you’re never going to achieve the profits that you need, because it’s too choppy, and you have all sorts of problems,” she said.

Base Operations addresses this problem by taking available data from a range of sources and pulling it together to inform threat profiles. Their technology is all about making sense of the myriad stream of information we encounter daily — taking the wash of news that we sometimes associate with “doom-scrolling” on social media, for instance, and combining it with other sources using machine learning to extrapolate actionable insights.

Those sources of information include “government statistics, social media, local news, data from partnerships, like NGOs and universities,” Siskind said. That data set powers their Micro Intelligence platform, and while the startup’s focus today is on helping enterprises keep people safe, while maintaining their operations, you can easily see how the same information could power everything from planning future geographical expansion, to tailoring product development to address specific markets.

Siskind saw there was a need for this kind of approach to an aspect of business that’s essential, but that has been relatively slow to adopt new technologies. From her vantage point two years ago, however, she couldn’t have anticipated just how urgent the need for better, more scalable enterprise security solutions would arise, and Base Operations now seems perfectly positioned to help with that need.

Feb
11
2021
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Intenseye raises $4M to boost workplace safety through computer vision

Workplace injuries and illnesses cost the U.S. upwards of $250 billion each year, according to the Economic Policy Institute. ERA-backed startup Intenseye, a machine learning platform, has raised a $4 million seed round to try to bring that number way down in an economic and efficient way.

The round was co-led by Point Nine and Air Street Capital, with participation by angel investors from Twitter, Cortex, Fastly and Even Financial.

Intenseye integrates with existing network-connected cameras within facilities and then uses computer vision to monitor employee health and safety on the job. This means that Intenseye can identify health and safety violations, from not wearing a hard hat to ignoring social distancing protocols and everything in between, in real time.

The service’s dashboard incorporates federal and local workplace safety laws, as well as an individual organization’s rules to monitor worker safety in real time. All told, the Intenseye platform can identify 30 different unsafe behaviors which are common within workplaces. Managers can further customize these rules using a drag-and-drop interface.

When a violation occurs and is spotted, employee health and safety professionals receive an alert immediately, by text or email, to resolve the issue.

Intenseye also takes the aggregate of workplace safety compliance within a facility to generate a compliance score and diagnose problem areas.

The company charges a base deployment fee and then on an annual fee based on the number of cameras the facility wants to use as Intenseye monitoring points.

Co-founder Sercan Esen says that one of the greatest challenges of the business is a technical one: Intenseye monitors workplace safety through computer vision to send EHS (employee health and safety) violation alerts but it also never analyzes faces or identifies individuals, and all video is destroyed on the fly and never stored with Intenseye.

The Intenseye team is made up of 20 people.

“Today, our team at Intenseye is 20% female and 80% male and includes four nationalities,” said Esen. “We have teammates with MSes in computer science and teammates who have graduated from high school.”

Diversity and inclusion among the team is critical at every company, but is particularly important at a company that builds computer vision software.

The company has moved to remote work in the wake of the pandemic and is using VR to build a virtual office and connect workers in a way that’s more immersive than Zoom.

Intenseye is currently deployed across 30 cities and will use the funding to build out the team, particularly in the sales and marketing departments, and deploy go-to-market strategies.

Feb
09
2021
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SentinelOne to acquire high-speed logging startup Scalyr for $155M

SentinelOne, a late-stage security startup that helps customers make sense of security data using AI and machine learning, announced today that it is acquiring high-speed logging startup Scalyr for $155 million in stock and cash.

SentinelOne sorts through oodles of data to help customers understand their security posture, and having a tool that enables engineers to iterate rapidly in the data, and get to the root of the problem, is going to be extremely valuable for them, CEO and co-founder Tomer Weingarten explained. “We thought Scalyr would be just an amazing fit to our continued vision in how we secure data at scale for every enterprise [customer] out there,” he told me.

He said they spent a lot of time shopping for a company that could meet their unique scaling needs and when they came across Scalyr, they saw the potential pretty quickly with a company that has built a real-time data lake. “When we look at the scale of our technology, we obviously scoured the world to find the best data analytics technology out there. We [believe] we found something incredibly special when we found a platform that can ingest data, and make it accessible in real time,” Weingarten explained.

He believes the real time element is a game changer because it enables customers to prevent breaches, rather than just reacting to them. “If you’re thinking about mitigating attacks or reacting to attacks, if you can do that in real time and you can process data in real time, and find the anomalies in real time and then meet them, you’re turning into a system that can actually deflect the attacks and not just see them and react to them,” he explained.

The company sees Scalyr as a product they can integrate into the platform, but also one which will remain a standalone. That means existing customers should be able to continue using Scalyr as before, while benefiting from having a larger company contributing to its R&D.

While SentinelOne is not a public company, it is a pretty substantial private one, having raised over $695 million, according to Crunchbase data. The company’s most recent funding round came last November, a $267 million investment with a $3.1 billion valuation.

As for Scalyr, it was launched in 2011 by Steve Newman, who first built a word processor called Writely and sold it to Google in 2006. It was actually the basis for what became Google Docs. Newman stuck around and started building the infrastructure to scale Google Docs, and he used that experience and knowledge to build Scalyr. The startup raised $27 million along the way, according to Crunchbase data, including a $20 million Series A investment in 2017.

The deal will close this quarter, at which time Scalyr’s 45 employees will join SentinelOne.

Feb
04
2021
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Google Cloud launches Apigee X, the next generation of its API management platform

Google today announced the launch of Apigee X, the next major release of the Apgiee API management platform it acquired back in 2016.

“If you look at what’s happening — especially after the pandemic started in March last year — the volume of digital activities has gone up in every kind of industry, all kinds of use cases are coming up. And one of the things we see is the need for a really high-performance, reliable, global digital transformation platform,” Amit Zavery, Google Cloud’s head of platform, told me.

He noted that the number of API calls has gone up 47 percent from last year and that the platform now handles about 2.2 trillion API calls per year.

At the core of the updates are deeper integrations with Google Cloud’s AI, security and networking tools. In practice, this means Apigee users can now deploy their APIs across 24 Google Cloud regions, for example, and use Google’s caching services in more than 100 edge locations.

Image Credits: Google

In addition, Apigee X now integrates with Google’s Cloud Armor firewall and its Cloud Identity Access Management platform. This also means that Apigee users won’t have to use third-party tools for their firewall and identity management needs.

“We do a lot of AI/ML-based anomaly detection and operations management,” Zavery explained. “We can predict any kind of malicious intent or any other things which might happen to those API calls or your traffic by embedding a lot of those insights into our API platform. I think [that] is a big improvement, as well as new features, especially in operations management, security management, vulnerability management and making those a core capability so that as a business, you don’t have to worry about all these things. It comes with the core capabilities and that is really where the front doors of digital front-ends can shine and customers can focus on that.”

The platform now also makes better use of Google’s AI capabilities to help users identify anomalies or predict traffic for peak seasons. The idea here is to help customers automate a lot of the standards automation tasks and, of course, improve security at the same time.

As Zavery stressed, API management is now about more than just managing traffic between applications. But more than just helping customers manage their digital transformation projects, the Apigee team is now thinking about what it calls ‘digital excellence.’ “That’s how we’re thinking of the journey for customers moving from not just ‘hey, I can have a front end,’ but what about all the excellent things you want to do and how we can do that,” Zavery said.

“During these uncertain times, organizations worldwide are doubling-down on their API strategies to operate anywhere, automate processes, and deliver new digital experiences quickly and securely,” said James Fairweather, Chief Innovation Officer at Pitney Bowes. “By powering APIs with new capabilities like reCAPTCHA Enterprise, Cloud Armor (WAF), and Cloud CDN, Apigee X makes it easy for enterprises like us to scale digital initiatives, and deliver innovative experiences to our customers, employees and partners.”

Feb
01
2021
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Weights & Biases raises $45M for its machine learning tools

Weights & Biases, a startup building tools for machine learning practitioners, is announcing that it has raised $45 million in Series B funding.

The company was founded by Lukas Biewald, Chris Van Pelt and Shawn Lewis — Biewald and Van Pelt previously founded CrowdFlower/Figure Eight (acquired by Appen). Weights & Biases says it now has more than 70,000 users at more than 200 enterprises.

Biewald (whom I’ve known since college) argued that while machine learning practitioners are often compared to software developers, “they’re more like scientists in some ways than engineers.” It’s a process that involves numerous experiments, and Weights & Biases’ core product allows practitioners to track those experiments, while the company also offers tools around data set versioning, model evaluation and pipeline management.

“If you have a model that’s controlling a self-driving car and the car crashes, you really want to know what happened,” Biewald said. “If you built that model years ago and you’ve run all these experiments since then, it can be hard to systematically trace through what happened” unless you’re using experiment tracking.

He described the startup as “an early leader” in this market, and as competing tools emerge, he said it’s also differentiated because it is “completely focused on the ML practitioner” rather than top-down enterprise sales. Similarly, he said that as machine learning has been adopted more widely, Weights & Biases is occasionally confronted by a “high-class problem.”

Weights & Biases screenshot

Image Credits: Weights & Biases

“We’re not interested in selling to companies that are doing machine learning for machine learning’s sake,” Biewald said. “With some companies, there’s a mandate from the CEO to sprinkle some machine learning in the company. That’s just really depressing to me, to not have any impact. But I would actually say the vast majority of companies that we talk to really do something useful.”

For example, he said agriculture giant John Deere is using the startup’s platform to continually improve the way it uses robotics to spray fertilizer, rather than pesticides, to kill weeds and pests. And there are pharmaceutical companies using the platform for how they model how different molecules will behave.

Weights & Biases previously raised $20 million in funding. The new round was led by Insight Partners, with participation from Coatue, Trinity Ventures and Bloomberg Beta. Insight’s George Mathew is joining the board of directors.

“I’ve never seen a MLOps category leader with such a high NPS and deep customer focus as Weights and Biases,” Mathew said in a statement. “It’s an honor to make my first investment at Insight to serve an ML practitioner user-base that grew 60x these last two years.”

The startup says it will use the funding to continue hiring in engineering, growth, sales and customer success.

Jan
27
2021
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Pinecone lands $10M seed for purpose-built machine learning database

Pinecone, a new startup from the folks who helped launch Amazon SageMaker, has built a vector database that generates data in a specialized format to help build machine learning applications faster, something that was previously only accessible to the largest organizations. Today the company came out of stealth with a new product and announced a $10 million seed investment led by Wing Venture Capital.

Company co-founder Edo Liberty says that he started the company because of this fundamental belief that the industry was being held back by the lack of wider access to this type of database. “The data that a machine learning model expects isn’t a JSON record, it’s a high dimensional vector that is either a list of features or what’s called an embedding that’s a numerical representation of the items or the objects in the world. This [format] is much more semantically rich and actionable for machine learning,” he explained.

He says that this is a concept that is widely understood by data scientists, and supported by research, but up until now only the biggest and technically superior companies like Google or Pinterest could take advantage of this difference. Liberty and his team created Pinecone to put that kind of technology in reach of any company.

The startup spent the last couple of years building the solution, which consists of three main components. The main piece is a vector engine to convert the data into this machine-learning ingestible format. Liberty says that this is the piece of technology that contains all the data structures and algorithms that allow them to index very large amounts of high dimensional vector data, and search through it in an efficient and accurate way.

The second is a cloud hosted system to apply all of that converted data to the machine learning model, while handling things like index lookups along with the pre- and post-processing — everything a data science team needs to run a machine learning project at scale with very large workloads and throughputs. Finally, there is a management layer to track all of this and manage data transfer between source locations.

One classic example Liberty uses is an eCommerce recommendation engine. While this has been a standard part of online selling for years, he believes using a vectorized data approach will result in much more accurate recommendations and he says the data science research data bears him out.

“It used to be that deploying [something like a recommendation engine] was actually incredibly complex, and […] if you have access to a production grade database, 90% of the difficulty and heavy lifting in creating those solutions goes away, and that’s why we’re building this. We believe it’s the new standard,” he said.

The company currently has 10 people including the founders, but the plan is to double or even triple that number, depending on how the year goes. As he builds his company as an immigrant founder — Liberty is from Israel — he says that diversity is top of mind. He adds that it’s something he worked hard on at his previous positions at Yahoo and Amazon as he was building his teams at those two organizations. One way he is doing that is in the recruitment process. “We have instructed our recruiters to be proactive [in finding more diverse applicants], making sure they don’t miss out on great candidates, and that they bring us a diverse set of candidates,” he said.

Looking ahead to post-pandemic, Liberty says he is a bit more traditional in terms of office versus home, and that he hopes to have more in-person interactions. “Maybe I’m old fashioned but I like offices and I like people and I like to see who I work with and hang out with them and laugh and enjoy each other’s company, and so I’m not jumping on the bandwagon of ‘let’s all be remote and work from home’.”

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.”

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