The Platform as a Service for Creating Synthetic Data​

Overcome costs and challenges in acquiring and using real-world data
for training machine learning and artificial intelligence systems.

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Rendered.ai is a PaaS designed for data scientists, engineers, and developers

  • Create and deploy unlimited, customized channels to generate synthetic datasets for ML/AI training and validation
  • Experiment with sensor models, scene content, and post-processing imagery effects
  • Characterize and catalog both existing and synthetic datasets
  • Download or move data to your own cloud repositories for processing and training
Here is some alt text​ Synthetic images from Rendered.ai helped validate the potential to test human oocyte viability through deep learning

Power innovation and increase productivity with synthetic data as a capability

  • Build custom pipelines to model diverse sensors and computer vision inputs​
  • Start quickly with free, customizable python sample code to model SAR, RGB satellite imagery, and more sensor types​
  • Experiment and iterate with flexible licensing that enables nearly unlimited content generation
  • Create labelled content rapidly in a hosted, high performance compute environment​
  • Enable collaboration between data scientists and data engineers with a no-code configuration experience​
Rendered.ai enables users to create synthetic datasets to meet AI training needs for data science at any scale​ Generate tens of thousands of images in a managed HPC environment
Rendered.ai uses 3D model content to create diverse CV training datasets from satellite data to microscopy​ Build scenarios with procedural world generation or bring your own reality capture models​

Get started quickly with our world building tools, or bring your own reality capture models and 3D content​

The Rendered.ai Platform uses industry-standard models and techniques to create an unlimited of variety of synthetic imagery for AI training optimization.

  • Procedurally modeled landscapes, vegetation, buildings, water bodies, and cities​
  • Reality capture meshes and models​
  • User and community contributed 3D assets​
  • Sample sensor models to generate simulated optical, radar, IR and other types of imagery output

Hosted collaboration for data scientists, engineers, and developers​

The Rendered.ai Platform enables collaborative workflows between data science team members.​

  • Easy-to-use graphical interface allows multiple users to configure and collaborate on data generation channels​
  • Generated synthetic datasets can be downloaded locally or shared to cloud-based storage for training and analysis
  • Flexible licensing models enable experimentation and iteration​
  • Integrate into enterprise CV training and AI analytics workflows with robust python and REST APIs​
Rendered.ai includes an easy-to-use web-based interface to create and configure imagery generation User friendly graphical interface for collaborative configuration of synthetic sensor data generation
Rendered.ai supports sensor-fusion CV training through configurability and content reuse Use the same scene and content to create synthetic datasets for multiple sensor modes

Scale to train AI on complex, constantly changing data fusion problems

Rendered.ai's cloud-native platform enables rapid innovation by teams needing to generate and exploit synthetic datasets.

  • Save time and cost by focusing on configuration and customization for data that fits business-specific AI training needs​
  • Create endless variations of data pipelines to accommodate sensor fusion scenarios, model sensors that don’t yet exist, and to simulate scenarios that are hard to reach​
  • Generate unlimited datasets to enable experimentation and tuning, then embed synthetic data generation in enterprise AI workflows​
  • Deploy data generation pipelines to high performance compute (HPC) environments and use standard tools such as AWS SageMaker and NVIDIA® TLT​

Industries

"Working with Rendered, we were able to use synthetic data to significantly improve computer vision algorithm performance for detecting economically important objects in satellite imagery. AP scores improved across the board for identifying rare and unusual objects when combining synthetic images with actual satellite imagery compared with using real images alone. The objects in our study have been historically difficult to identify using automated algorithms but represent high value information for monitoring and reporting."

"We saw that synthetic data is a critical capability for computer vision algorithm development and for optimizing detection performance to extract vital information from real sensor-based data."

James Crawford, Orbital Insight
Founder, Chairman, CTO

Orbital Insight Technical Report
Using Synthetic Imagery to Train Detection Models of Rare Objects in Satellite Imagery

Media

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