AI is just software that uses data instead of code. We provide a Common Application Framework to produce physics-based synthetic datasets for AI training and validation.Get in Touch
Nathan Kundtz, Rendered.ai CEO, April 12, 2021 10:00am PDT (UTC-7)
In our experience, lack of diversity in the dataset is the root cause of most synthetic data failures. To address this issue, we incorporate procedural models over static ones whenever possible:
We provide an easy-to-use graphical interface for dataset generation AND a complete set of APIs for access wherever you need it.
Data can be downloaded locally or used with cloud-based pipelines (including directly to your AWS S3 bucket) keeping data residency near a global set of analytics tools.
Our architecture is cloud native; meaning almost instantly scalable compute environments are at your fingertips for both dataset generation as well as training and AI deployment.
Our shared simulation domains allow us to produce cross-domain, integrated, multi-sensor simulations to support robust data-fusion models from a single simulation enivronment.
All of our rendering engines are GPU-accelerated (including our full-wave Synthetic Aperture Radar engine!) meaning you'll have access to massive compute available via GPUs.
If we don’t have something you need in our tools library, we can build it for you or give you API hooks into the simulation environment.
TESLA VS WAYMO – How amenable is each approach to physics based synthetic data? An Artificial Intelligence POV
AI INTRO – Get to know more about the needs, challenges and benefits of a data engineering approach to prove-able AI.
BELLEVUE, Wash., February 11, 2021
BELLEVUE, Wash., October 6, 2020
Drop us a line, ask a question, or request a demo or login.
We’re happy to help in any way.