nemo-data-designer-plugin

You can generate synthetic datasets by using LLM-powered samplers and custom generators to automate configuration, schema validation, and the creation of diverse data distributions for model training.

You can build portable data generation pipelines that support iterative design and local previewing, then seamlessly submit the same configurations for large-scale, cluster-based generation tasks.

Yes, synthetic data generation integrates with Nemotron Personas and Inference Gateway providers to ensure high-fidelity, diverse, and schema-validated output across your generated datasets.

Yes, the workflow uses Pydantic dependencies to enforce schema validation automatically, ensuring that all generated records and custom data distributions remain compliant before pipeline execution.

The best way is to use an automated pipeline that configures LLM-powered samplers to generate large volumes of records while applying specific demographic and issue-type distributions for schema-compliant output.

Local previewing supports iterative design for your pipeline, but large-scale generation tasks require cluster-based submission to execute the full workflow and produce massive synthetic datasets efficiently.