Synthetic Data Generation with GenAI

Use GenAI to generate realistic, privacy-safe data for training.

Pillar

Data – Readiness, Governance, Quality & Ethics

Overview

This course explores how Generative AI can be leveraged to create synthetic datasets that mimic real-world data while protecting privacy and compliance requirements. Participants learn techniques for generating, validating, and applying synthetic data in AI training and testing scenarios, enhancing model robustness without exposing sensitive information.

Learning Objectives

Participants will be able to:

• Understand the principles and benefits of synthetic data generation

• Use GenAI tools to create privacy-preserving synthetic datasets

• Validate synthetic data quality and representativeness

• Apply synthetic data in AI model training and evaluation

• Address ethical and compliance considerations related to synthetic data

Target Audience

• Data scientists and machine learning engineers

• AI developers and researchers

• Data privacy and compliance professionals

• AI project managers

Duration

20 hours over 4 days (5 hours per day)

Delivery Format

• Hands-on tutorials with GenAI synthetic data tools

• Case studies on synthetic data applications

• Group discussions on privacy and ethical challenges

• Practical exercises generating and validating datasets

Materials Provided

• Access to synthetic data generation tools and scripts

• Validation checklists and quality metrics

• Ethical guidelines and compliance frameworks

• Example datasets for practice

Outcomes

• Practical skills in generating synthetic data with GenAI

• Ability to evaluate and improve synthetic dataset quality

• Increased confidence in using synthetic data for AI projects

• Awareness of privacy and ethical implications

Outline / Content

Day 1: Introduction to Synthetic Data and Generative AI

• Fundamentals of synthetic data and its importance

• Overview of GenAI techniques for data generation

Day 2: Generating Synthetic Data with GenAI Tools

• Hands-on use of popular synthetic data generators

• Techniques for balancing realism and privacy

Day 3: Validating and Using Synthetic Data

• Methods to assess quality and representativeness

• Integration of synthetic data in AI model workflows

Day 4: Ethical, Legal, and Practical Considerations

• Addressing privacy, bias, and regulatory concerns

• Best practices for synthetic data governance and management