Dataset Development & Annotation Acceleration

This service delivers high-quality annotated datasets using IRIS’s candidate-driven validation workflow.

Instead of manual frame-by-frame labeling, a small set of seeded examples is used to automatically surface likely relevant samples. Human effort is focused on validation, not discovery, enabling significantly faster dataset growth while maintaining quality.

The result is a structured dataset aligned to real-world conditions, including edge cases and difficult scenarios that are often missed in traditional labeling workflows.

This service is designed to produce training-ready datasets efficiently, reducing time to model development and improving downstream performance.

AWS Environment and Delivery Dataset processing and workflow execution are performed in AWS using Amazon EC2 compute resources, with data and annotation artifacts stored in Amazon S3. Final annotated datasets are delivered in formats compatible with Amazon SageMaker data ingestion and training workflows. Upon request, IRIS can deliver completed dataset artifacts directly to the customer’s AWS account in Amazon S3 for immediate use in SageMaker training pipelines.