Synthex User Guide

Prepare, profile, transform, and generate synthetic datasets for model training, evaluation, and privacy-safe testing.

Who This Guide Is For

Where To Go

Page Use It For
/synthex Synthex overview and activity.
/synthex/datasets Imported datasets and dataset status.
/synthex/dataset-registry Reusable dataset catalog.
/synthex/data-profiles Schema, statistics, and quality profile management.
/synthex/data-recipes Reusable data-cleaning and transformation recipes.
/synthex/data-generator Generate synthetic records.
/synthex/unified-generator Generate data with guided method selection.
/synthex/export-wizard Export generated datasets for training or downstream use.
/synthex/training-feedback Review feedback from training and evaluation consumers.

Core Concepts

Concept Meaning
Dataset A managed data collection with schema, versions, profile, source, and quality state.
Profile A statistical and structural description used for validation and generation.
Recipe A reusable transformation pipeline for cleaning, normalization, filtering, encoding, or augmentation.
Generator A synthetic data method selected manually or automatically based on use case and modality.
Privacy settings Differential privacy and sensitive-field controls used to protect source data.

Common Workflows

Generate privacy-safe tabular data

  1. Import or select a source dataset.
  2. Run or review the data profile.
  3. Apply a cleaning recipe if quality issues are present.
  4. Open the generator and choose the privacy-preserving use case.
  5. Set record count and privacy parameters.
  6. Run generation and review quality metrics.
  7. Export the dataset to Model Training or object storage.

Prepare edge-case test data

  1. Open Task Specs or Failure Triggers.
  2. Define the rare condition or failure mode.
  3. Generate a targeted dataset.
  4. Validate profile and label distribution.
  5. Send the result to Evaluation or Model Training.

Best Practices