Generative AI for Data Synthesis

• An understanding of basic machine learning concepts
• Experience with Python programming
• Familiarity with data science workflows

Audience

• Data scientists
• AI practitioners

Generative AI is an area of artificial intelligence that focuses on creating new content, data, and models that can mimic real-world distributions. Synthetic data is a substitute for real data.

This instructor-led, live training (online or onsite) is aimed at intermediate-level data professionals who wish to create synthetic datasets for AI model training where real data is scarce or sensitive.

By the end of this training, participants will be able to:

• Understand the role and creation of synthetic data in Generative AI.
• Implement Generative AI models to produce high-quality synthetic data.
• Assess the quality and utility of synthetic datasets.
• Navigate the ethical and legal considerations in using synthetic data.
• Apply synthetic data strategies to real-world AI challenges.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Generative AI

• Defining generative AI
• Overview of generative models (GANs, VAEs, etc.)
• Applications and case studies

The Need for Synthetic Data

• Limitations of real data
• Privacy and security concerns
• Enhancing AI model robustness

Generating Synthetic Data

• Techniques for synthetic data generation
• Ensuring data quality and diversity
• Practical workshop: Creating your first synthetic dataset

Evaluating Synthetic Data

• Metrics for assessing synthetic data quality
• Comparing synthetic vs. real data performance
• Case study analysis

Ethical and Legal Aspects

• Navigating the ethical landscape
• Legal frameworks and compliance
• Balancing innovation with responsibility

Advanced Topics in Data Synthesis

• Synthetic data for unsupervised learning
• Cross-domain data synthesis
• Future trends in generative AI

Capstone Project

• Applying knowledge to real-world scenarios
• Developing a synthetic data strategy
• Assessment and feedback

Summary and Next Steps