While more companies are investing in AI, many are struggling to put AI-powered projects into production, let alone achieve meaningful ROI.
There are many challenges. But one thing that often repeats is data management. The data that enterprises need to train, run, and fine-tune their AI models is unorganized, siled, and unoptimized. In a 2022 survey by Great Expectations, an open source data benchmarking platform, 77% of organizations said they were concerned about data quality.
Startups promising to solve these data problems are raising money.
On Wednesday, Weka, a platform for building data pipelines that handle a variety of data sources, types, and sizes, raised $100 million in a two-part ($100 million and $40 million) Series E round led by Valor Equity Partners. announced that it had raised $40 million. , Nvidia, Norwest Venture Partners, Micron Ventures, Qualcomm Ventures, Hitachi Ventures and others. This oversubscribed round values Weka at $1.6 billion post-money, double the company's previous valuation.
Weka CEO Liran Zvibel and Weka's other co-founders Maor Ben-Dayan and Omri Palmon met while founding data storage startup XIV. XIV he was acquired by IBM in 2007 for his $350 million. The trio remained with IBM for many years, but were eventually left to pursue other independent ventures.
But data management issues continued to plague Zvibel, he says.
“I was frustrated and disillusioned as I watched customers being forced to use disparate and siled data infrastructure solutions that were complex, wasteful and costly to deploy, manage and maintain.” he said. “This problem has become particularly apparent with the rise of cloud computing and the emergence of high-performance computing, machine learning, and early AI workloads.”
So in 2013, Zvibel hired Ben-Dayan and Palmon to build a new set of data tools. This set could lead to a better approach to storing, managing, and moving data.
“We envisioned a platform powerful enough to support the performance demands of next-generation computing hardware and large-scale, data-intensive workloads in demanding distributed environments,” said Zvibel. says Mr. “We knew that to meet the needs of modern workloads, we needed to process tens of terabytes of data and be able to deploy anywhere.”
Weka's core product is a parallel file system, a type of distributed file system that allows data tasks (such as copying files) to be distributed and coordinated across multiple locations (such as servers and workstations) simultaneously. In addition to this, Weka sells services and capabilities that support AI and machine learning, visual effects, and high-performance computing workloads in environments across on-premises data centers, public clouds, and hybrid clouds.
Zvibel claims that one of the key benefits of Weka's architecture is that it can speed up the training of AI models by reducing the time it takes to copy data between storage locations. “Typical generative AI data pipelines include multiple steps that copy data sets, which wastes critical training time,” he said. “Weka allows us to train our models faster because we can constantly feed data to our training hardware.”
Weka competes with data platforms such as DataDirect, Pure Storage, NetApp, and Vast Data. Vast is one of the more formidable companies, completing a $118 million Series E funding round in December 2023, tripling the startup's valuation to $9.1 billion.
But Weka appears to be holding its own, with a customer base of more than 300 brands, including AI startup Stability AI, 11 Fortune 50 companies, and several undisclosed national and international government agencies. Masu.
Despite its relatively large workforce (about 400 employees worldwide, with plans to increase that number by 25% next year), Zwibel said the Silicon Valley-based company has The company said it “projects” to have cash flow in the black by the end of the year. .
“The amount raised was calculated based on favorable market conditions and positive investor interest, and we were able to raise the amount on very favorable and advantageous terms for Weka,” he added. “Our average burn rate is expected to be less than $500,000 per month by the time we reach that milestone. Our annual recurring revenue is in excess of $100 million and continues on a hyper-growth trajectory. Masu.”