Synthetic Data Validation

*Overview: Synthetic data is a powerful alternative for training and evaluating AI models, especially when real data is limited, sensitive, or costly. However, ensuring synthetic datasets meet the necessary quality standards is critical for their effectiveness. Our Synthetic Data Validation service assesses synthetic data across key dimensions such as fidelity, utility, bias, and privacy risk. How it works: Clients upload their synthetic data through AWS S3 or API connections. Our validation process includes: Comparing the statistics and feature relationships of synthetic data against real-world data Using machine learning techniques to test how well the synthetic data performs in real tasks Analyzing privacy risks by detecting potential data leaks and verifying compliance Having expert humans review the data for deeper qualitative insights Providing clear reports that highlight issues, flag risks, and offer actionable ways to improve your data Deliverables: Detailed reports with scores on fidelity, usefulness, and privacy of your synthetic data Annotated data pointing out areas that need improvement Recommendations to enhance your data generation process, reduce bias, and improve privacy protections Options for ongoing monitoring so you can keep your data quality high as it changes over time Quality & Metrics: We measure synthetic data quality by checking: How closely your synthetic data matches real data distributions How well models trained on synthetic data perform on real tasks Whether any biases appear in the data Privacy safeguards like preventing sensitive info leaks Integrations & Formats: Compatible with CSV, JSONL, and SageMaker Ground Truth formats. Seamlessly integrates with AWS S3, SageMaker, and orchestration systems for scalable workflows. Security & Compliance: Contractually enforced data privacy with encrypted storage, strict access controls, and secure lifecycle management *