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.”

Mar
13
2019
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Determined AI nabs $11M Series A to democratize AI development

Deep learning involves a highly iterative process where data scientists build models and test them on GPU-powered systems until they get something they can work with. It can be expensive and time-consuming, often taking weeks to fashion the right model. New startup Determined AI wants to change that by making the process faster, cheaper and more efficient. It emerged from stealth today with $11 million in Series A funding.

The round was led by GV (formerly Google Ventures) with help from Amplify Partners, Haystack and SV Angel. The company also announced an earlier $2.6 million seed round from 2017, for a total $13.6 million raised to date.

Evan Sparks, co-founder and CEO at Determined AI, says that up until now, only the largest companies like Facebook, Google, Apple and Microsoft could set up the infrastructure and systems to produce sophisticated AI like self-driving cars and voice recognition technologies. “Our view is that a big reason why [these big companies] can do that is that they all have internal software infrastructure that enables their teams of machine learning engineers and data scientists to be effective and produce applications quickly,” Sparks told TechCrunch.

Determined’s idea is to create software to handle everything from managing cluster compute resources to automating workflows, thereby putting some of that big-company technology within reach of any organization. “What we exist to do is to build that software for everyone else,” he said. The target market is Fortune 500 and Global 2000 companies.

The company’s solution is based on research conducted over the last several years at AmpLab at the University of California, Berkeley (which is probably best known for developing Apache Spark). It used the knowledge generated in the lab to build sophisticated solutions that help make better use of a customer’s GPU resources.

“We are offering kind of a base layer that is scheduling and resource sharing for these highly expensive resources, and then on top of that we’ve layered some services around workflow automation.” Sparks said the team has generated state of the art results that are somewhere between five and 50 times faster than the results from tools that are available to most companies today.

For now, the startup is trying to help customers move away from generic kinds of solutions currently available to more customized approaches, using Determined AI tools to help speed up the AI production process. The money from today’s round should help fuel growth, add engineers and continue building the solution.

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