Synthetic Data Generation & Use in AI

Module 1: Introduction to Synthetic Data

• Define synthetic data and distinguish its types including tabular, image, text, and time-series formats
• Analyze the benefits of synthetic data over real data in terms of privacy, cost, and scalability
• Evaluate scenarios to determine when and when not to use synthetic data in AI projects

Module 2: Tools and Techniques for Data Generation

• Explore leading synthetic data generators including Gretel, MOSTLY AI, and SDV
• Implement GANs, VAEs, and LLMs for generating high-fidelity synthetic datasets
• Apply prompt-based data synthesis techniques for NLP and domain-specific tasks

Module 3: Generating Synthetic Data

• Build GAN-based generation pipelines for synthetic images and video content
• Generate synthetic tabular data using statistical models and simulation frameworks
• Balance and augment existing datasets with strategically synthesized samples

Module 4: Evaluation and Quality Assurance

• Measure utility metrics to assess how useful synthetic data is for downstream AI tasks
• Implement privacy metrics including differential privacy, k-anonymity, and membership inference tests
• Detect fidelity gaps, diversity limitations, and hidden biases in generated datasets

Module 5: Deploying Synthetic Data in AI Workflows

• Integrate synthetic data seamlessly into model training and validation pipelines
• Design augmentation strategies for low-data and imbalanced classification scenarios
• Conduct adversarial testing and model debugging using synthetic scenario generation

Module 6: Ethics, Governance, and Real-World Impact

• Navigate regulatory considerations and emerging industry standards for synthetic data use
• Practice transparency, disclosure, and responsible deployment in AI systems
• Complete a capstone project designing and evaluating a full synthetic data pipeline

Module 7: Advanced Generative Models and Diffusion Techniques

• Harness diffusion models for high-quality synthetic image and multimodal data generation
• Fine-tune large language models for domain-specific synthetic text corpus creation
• Optimize generative pipelines for computational efficiency and output quality