The company, backed by Ubiquity Ventures and trusted by some of the sharpest minds in computer vision, helps engineers find, curate, diagnose, and remedy enormous datasets faster than ever before
SceneBox, a start-up bringing production readiness to machine learning (ML) research and applications, is officially opening up its DataOps platform today. SceneBox helps engineers spend more time building models and less time wrangling data. The SceneBox platform, which is already being used by some of the leading computer vision applications with nationwide and global impact, provides users with effective methods of improving ML models through its cutting-edge features.
According to Allied Market Research, the global AI in computer vision market was estimated at $9.2 billion in 2020 and is expected to exceed $200 billion by 2030, a growth of 40% annually for each of the next 10 years.
The training of production-grade computer vision solutions relies heavily on high-quality data. To curate such high-quality data, ML teams have to search, organize, and review massive amounts of unstructured data which is extremely difficult and time consuming. SceneBox was built for the data-centric development of computer vision models and is flexible in data management. SceneBox can manage simple image datasets in a cloud bucket to complex multi-sensor spatiotemporal data from a variety of cameras, lidars, and IMU sensors typical in an autonomous vehicle or robotics application. SceneBox can serve the data from any cloud and can work with real data and synthetic data alike.
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“It is now crystal clear that the next generation of software is trained, not written, so SceneBox is well-positioned as the hub to manage all this machine learning training data”, said Sunil Nagaraj, Managing Partner at Ubiquity Ventures. “It’s rare to find a company at the intersection of several very timely trends, so it was an easy decision to invest in CEO Yaser Khalighi’s vision at SceneBox.”
SceneBox’s customers fall into a number of vertical industries including autonomous vehicles, security cameras, insurance technology, retail, satellite imagery, and pharmaceuticals. SceneBox has the largest number of integrations among its competitors with labeling providers such as LabelBox, Scale, SuperAnnotate, SageMaker GT, CVAT, and others.
SceneBox is already delivering business value to world-class computer vision teams. “We recently integrated with SceneBox and are excited to incorporate it into our DataOps workflow. It takes the pain out of data management by giving us a simple and intuitive interface to work with” said Julius Simonelli, a lead Machine Learning Engineer at Betterview. Betterview is harnessing the power of computer vision to analyze vast amounts of aerial imagery and serve insights to insurance carriers. Julius added “the integration has been smooth. Yaser and his team have been incredibly responsive and willing to tailor to our specific needs. SceneBox is already a great tool and continues to get better as they continuously update it with new features”.
“There is no single computer vision engineer that has not felt the acute pain of data wrangling for training and improvement of vision models,” said Yaser Khalighi, founder and CEO, SceneBox. “We built SceneBox to eliminate this pain. We are seeing tremendous interest in the computer vision space. In the past 6 months, SceneBox’s number of users and data under management has grown 8 fold and 20 fold, respectively.”
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