The physics-based leading synthetic data platform provider, Rendered.ai, announced availability of Rendered.ai Platform as a Service (PaaS) for synthetic data engineers and computer vision scientists. The platform includes a development environment, post-processing tools, and a complete synthetic data stack. These technologies allow users to create engineered datasets to avoid the gaps and biases that might exist when training AI using actual data.
Rendered.ai recently received funding from Space Capital, Tectonic Ventures, Congruent Ventures, Union Labs, and Uncorrelated Ventures to develop the world’s first publicly available artificial intelligence (AI) tool. Users may construct simulated settings by combining their digital sensor models with 2D and 3D information.
To help others get started with synthetic data, Rendered.ai has developed an open-source sample application. Commercial partners focused on synthetic x-ray data for security purposes and education teams focusing on life sciences microscopes have partnered with the firm. It can give visible wavelengths, hyperspectral imaging, and synthetic aperture radar data for remote sensing Earth studies.
Real-world datasets based on 3D environment simulations can be utilized to improve AI performance while reducing the volume of data required to train AI. Rendered.ai includes flawless tagging and a carefully designed distribution of training features, which are difficult or impossible to attain in actual datasets.
“Leading global companies have spent years building proprietary synthetic data platforms. Rendered.ai brings that capability to the rest of the industry.,” said Nathan Kundtz, founder and CEO, Rendered.ai. “A PaaS enables our customers to incorporate synthetic data generation as an evolving capability throughout AI workflows, going beyond individual projects to generate single datasets.”
“It’s an incredible time to be joining the Rendered.ai team when synthetic data usage is positioned to dominate AI applications,” said Andrews. “Many of my past customers and partners have struggled with data scarcity and quality, causing project failures in diverse industries such as geospatial, construction, and life sciences. By enabling customers to generate simulated datasets using digital twins of their sensors and environments, our unique approach enables customers to train, validate, and innovate AI applications, even in cases where they may not be able to obtain real sensor-based data.”
Government, commercial, and academic research users are already using the Rendered.ai PaaS. The model is equipped with an open-source SDK (Software Development Kit) that allows developers to create their own sensor models. It also includes a hosted job management feature that allows users to run virtually infinite jobs in the AWS cloud.
The new PaaS will enable users to use synthetic data in their corporate AI training and testing workflows. Customers will get access to a continuous, fine-tuned capacity to generate simulated or synthetic data for AI applications.