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Synthetic Data Generation

YData's Synthetic data Generation capabilities leverages state-of-the-art generative models to create high-quality artificial data that replicates real-world data properties. Regardless it is a table, a database or a text corpus, this powerful capability ensures privacy, enhances data availability, and boosts model performance across all industries. In this section discover how YData's Synthetic Data solutions can transform your Data & AI initiatives.

What is Synthetic Data?

Synthetic data is artificially generated data that mimics the statistical properties and structure of real-world data without directly copying it. It is created using algorithms and models designed to replicate the characteristics of actual data sets. This process ensures that synthetic data retains the essential patterns and relationships present in the original data, making it a valuable asset for various applications, particularly in situations where using real data might pose privacy, security, or availability concerns. It can be used for:

  • Guaranteeing privacy and compliance when sharing datasets (for quality assurance, product development and other analytics teams)
  • Removing bias by upsampling rare events
  • Balancing datasets
  • Augment existing datasets to improve the performance of machine learning models or use in stress testing
  • Smartly fill in missing values based on context
  • Simulate new scenarios and hypothesis

The benefits of Synthetic Data

Leveraging synthetic data offers numerous benefits:

  • Privacy and Security: Synthetic data eliminates the risk of exposing sensitive information, making it an ideal solution for industries handling sensitive data, such as healthcare, finance, and telecommunications.
  • Data Augmentation: It enables organizations to augment existing data sets, enhancing model training by providing diverse and representative samples, thereby improving model accuracy and robustness.
  • Cost Efficiency: Generating synthetic data can be more cost-effective than collecting and labeling large volumes of real data, particularly for rare events or scenarios that are difficult to capture.
  • Testing and Development: Synthetic data provides a safe environment for testing and developing algorithms, ensuring that models are robust before deployment in real-world scenarios.

Synthetic Data in YData SDK

YData SDK offers robust support for creating high-quality synthetic data using generative models and/or through bootstrapping. The package is designed to address the diverse needs of data scientists, engineers, and analysts by providing a comprehensive set of tools and features.

How does ydata-sdk generate synthetic data?

YData’s SDK implements a structured generative process designed to create high-fidelity synthetic datasets while minimizing manual configuration from the user. This process can be summarized in three main stages:

  1. Preprocessing: Input data is prepared by handling missing values, scaling features, and applying necessary encodings to ensure consistency.
  2. Dimensionality Transformation: Latent feature representations are learned to capture complex dependencies and reduce redundancy in the original dataset.
  3. Generative Modeling: Generative models are trained to reproduce both the statistical distributions and structural relationships of the input data.

An internal meta-search procedure evaluates the datasets characteristics and selects the most suitable one for the dataset at hand. This allows the synthesizers to adapt to different data behaviours, without requiring users to manually select architectures or tune hyperparameters.

Underlying models

YData leverages proprietary generative approaches, including:

  • Generative Adversarial Networks (GANs): Capture complex, high-dimensional data distributions by training adversarial networks that generate and discriminate synthetic samples.
  • Variational Autoencoders (VAEs): Learn latent representations to approximate complex feature distributions.
  • Bayesian Networks: Model probabilistic dependencies and conditional relationships among variables.

Core Features

Data Generation

  • High-fidelity synthetic data
  • Distribution preservation
  • Relationship maintenance
  • Privacy compliance

Use Cases

  • Analytics and reporting
  • Machine learning training
  • Privacy-preserving sharing
  • Testing and development

Getting Started Examples

For practical examples of using synthetic data generation, check out our Getting Started guides: