Outline

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

Outline

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

Outline

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

Outline

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

Outline

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

Outline

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