As the advent of machine learning continues to disrupt a swathe of industries, one of the things that is becoming increasingly clear is that machine learning needs lots of high-quality data to work well. According to the findings of a recently released survey, 99% of respondents reported having had an ML project completely canceled due to insufficient training data, and 100% of respondents reported experiencing project delays as a result of insufficient training data. Using synthetic data is one approach to get around the issues associated with obtaining and using high-quality data from the real world. We caught up with Rendered.ai Founder and CEO Nathan Kundtz to learn more about the use cases the platform can serve, and how it works under the hood.

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.