Synthetic Data
What is Synthetic Data?
Synthetic data is artificially generated information that mimics real-world datasets, enabling AI models to learn without relying solely on sensitive or limited real data. Also called simulated data, it plays a critical role in Edge AI and edge computing applications.
Why Is It Used?
Synthetic data is used to train AI models when real-world data is scarce, sensitive, or expensive to collect. It helps enhance privacy, reduce bias, and accelerate AI model development, especially for devices operating at the edge.
How Is It Used?
Model training: AI models are trained using synthetic data to improve accuracy.
Testing & validation: Simulated datasets evaluate AI systems before real-world deployment.
Scenario simulation: Edge devices can predict outcomes under various conditions using generated data.
Types of Synthetic Data
Fully synthetic: Entirely computer-generated datasets.
Partially synthetic: Combines real and simulated data.
Augmented synthetic: Enhances real data with simulated variations for robustness.
Benefits of Synthetic Data
Privacy-friendly: Eliminates risk of exposing sensitive data.
Cost-efficient: Reduces time and resources for data collection.
Scalable & diverse: Generates large, varied datasets for more robust Edge AI models.
Faster deployment: Speeds up AI training and deployment at edge nodes.