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Accelerate AI agent development with high-quality, domain-specific synthetic data using NVIDIA's NeMo Data Designer.

NVIDIA NeMo Data Designer facilitates the generation of synthetic data to train and evaluate agentic AI models. It addresses the scarcity, sensitivity, and cost challenges associated with real-world data. The tool allows users to design custom synthetic datasets from scratch or using existing example data. Users can configure LLMs and seed datasets to diversify the synthetic data, maintaining the patterns and characteristics of real-world data. The platform supports structured data generation with user-defined schemas, enabling the creation of high-fidelity synthetic documents for tax form validation, legal documents, and mortgage approvals. NeMo Safe Synthesizer ensures privacy-safe data generation, complying with regulations like HIPAA and GDPR, which allows seamless access to synthetic medical data. It also provides validation and evaluation tools, including automated metrics and LLM-based judges, to ensure high data quality.
NVIDIA NeMo Data Designer facilitates the generation of synthetic data to train and evaluate agentic AI models.
Explore all tools that specialize in data augmentation. This domain focus ensures NVIDIA NeMo Data Designer delivers optimized results for this specific requirement.
Allows users to design datasets from scratch or use existing example data, configuring models to generate data meeting specific domain requirements.
Leverages large language models (LLMs) to generate realistic and diverse synthetic data, capturing nuanced language and rare edge cases.
Uses NeMo Safe Synthesizer to create privacy-safe versions of sensitive data, compliant with regulations like HIPAA and GDPR.
Supports the design of structured data with user-defined schemas, enabling the creation of high-fidelity synthetic documents for various applications.
Provides comprehensive validation and evaluation tools, including automated metrics and LLM-based judges, to ensure high-quality synthetic data generation.
1. Connect and customize models for Synthetic Data Generation (SDG) in NeMo Data Designer.
2. Configure seed datasets to diversify the dataset and match specific domain patterns.
3. Design the structure and content of synthetic datasets by defining columns.
4. Configure LLM-generated columns with prompts and structured outputs.
5. Generate a small sample for validation and refine the design based on preview results.
6. Scale up to create a full dataset once the design meets the requirements.
7. Evaluate the quality of the data using automated metrics and LLM-based judges.
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