In data-scarce or biased environments, synthetic data is essential for building scalable, ethical, and high-performing AI. This workshop provides practical, hands-on training in generating, validating, and deploying synthetic datasets across tabular, image, and text formats. Learn how to seamlessly integrate simulation tools and data pipelines into your workflows to drive innovation in finance, healthcare, autonomous systems, and more.

What You’ll Learn

• Techniques for generating synthetic data across structured, visual, and language-based formats.
• Applications of synthetic data in simulation environments, utilizing reinforcement learning, and privacy-preserving model training.
• Validation strategies and deployment use cases in regulated and high-risk domains such as finance, healthcare, and autonomous systems.

Key Takeaways

• Tools and templates for synthetic data generation, validation, and integration into AI pipelines.
• A risk and compliance checklist to ensure responsible use of synthetic data in production environments.
• A roadmap for embedding synthetic data into model development, testing, and deployment workflows.

Who Should Attend

Data scientists, ML engineers, compliance leads, and technical teams working with sensitive, limited, or regulated datasets in:

• Finance
• Healthcare
• Autonomous Systems
• Research & Development