Rendered.ai: A hosted PaaS for generating Synthetic Data
Rendered.ai is pleased to announce the general availability of our hosted platform for synthetic data generation. The Rendered.ai Platform-as-a-Service (PaaS) is for data scientists, data engineers, and developers who need to create and deploy unlimited, customized synthetic data generation for machine learning and artificial intelligence workflows, especially focused on Computer Vision (CV) applications.
To explore Rendered.ai, request access here:
Our mission is to be the driving force behind entirely new types of data that empower faster and better decision making and insight while fostering an AI community that helps to achieve a fairer and more sustainable future.
Synthetic data can be used to reduce expense, close gaps, and overcome bias, security, and privacy issues when compared with the use or acquisition of real-world data. Mitigating these issues can have positive impacts on customers’ ability to innovate, costs of data acquisition, and even reduce the impact of real data collection on the environment.
Rendered.ai enables customers to create and exploit synthetic data by providing a configuration environment, samples, and cloud resources to quickly get started
- generating 100% labeled, CV-based synthetic data fit for their AI and ML applications,
- creating many large datasets with the use of high-performance compute, and
- providing tools to characterize and catalog existing and synthetic datasets.
Rendered.ai has been used to generate synthetic satellite imagery, Synthetic Aperture Radar (SAR) data, x-ray data, security video, and many more data types. You can even generate your own sample synthetic imagery using our Example Channel immediately after you sign in!
Access to our PaaS includes code examples and tutorials for developers who need to build synthetic data channels (custom applications), a web interface that enables the data scientist and CV engineer to reconfigure and run data generation, and APIs for integrating synthetic data generation into enterprise workflows.
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