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The Platform is a PaaS that enables your organization to add synthetic data as an enterprise capability. With you can create and access data that is engineered for your specific problem sets, as often and as much as you need, to help with AI and ML workflows for training, tuning, bias detection, and innovation.

What is is a Platform as a Service (PaaS) that enables data scientists, data engineers, and developers to create custom synthetic data applications, called channels, and to run them in the cloud to generate as much data as they want. Most of the data that is generated using is imagery data that can be used for Computer Vision (CV) AI-based workflows. Other types of synthetic data can be generated by the Platform and that is completely customizable by the channel developer.

Benefits of a PaaS

A PaaS removes the need for a user to maintain their own hardware, infrastructure, and application execution environment to run software systems and custom code. In the case of, we provide access to identity, cloud compute, data storage, and an SDK to execute your custom synthetic data pipeline in a cloud-based, high performance compute environment without requiring you to manage and maintain all of the infrastructure and software.

Why do I need a PaaS for synthetic data?

Many organizations who try synthetic data initially believe that they can generate or purchase single datasets for their AI workflows. However, these users soon discover that when they need more data for additional bias detection, to train for unexpected rare entities or scenarios, or when they want to apply AI to a whole new product, they will need to acquire more one-off datasets and may have lost domain knowledge or artifacts from their original investigations.

The PaaS enables users to access synthetic data as a service. Users can create, use, and store their domain knowledge and techniques as content and channels in , then update or branch them as their needs change. If a user then needs to create a new dataset for an entirely different AI problem set, they can do so, even incorporating access to datasets into automated workflows and remote systems through the SDK which allows access to the cloud-hosted PaaS.

Major components of the PaaS has three main experiences for data scientists, data engineers, and developers:

  • Web-based experience for configuration, collaboration, job execution, and dataset management

  • A development environment and samples to create synthetic data pipelines

  • A SDK for remote or integrated job execution and automation

Web-based experience for configuration and job execution

The main interface for interacting with to run and configure jobs is a web-based user interface that allows data scientists to run sample channels provided to emulate various sensors, configure graphs in a no-code experience to configure scenes and various parameters, then to run jobs for synthetic data generation and manage the output datasets.

Development environment and Example code provides a Docker-based development environment and example code to help data engineers and data scientists to customize synthetic data channels of their own.

An SDK for job execution and automation provides a SDK for developers to remotely create, execute, and access the output of synthetic data channels to accomplish batch processing and automated data provisioning to AI workflows.

Collaboration, account information, and billing

The same web interface that is used to configure graphs and run jobs also has settings and configuration for users to set and monitor billing information, upgrade, and manage and invite collaborators who are members of the organization.

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