Beyond Real-World Limitations: Synthetic Data Generation for Improved ML Performance

In this presentation, we tackle one of the most pressing challenges in applied AI: creating accurate, customized models for scenarios with limited data. Our approach combines the generative capabilities of large language models with the flexibility of AI agents to produce and validate synthetic, domain-specific training data. We’ll explore how this method overcomes the limitations of traditional autoML solutions and the prohibitive costs of custom LLMs. We’ll demonstrate how our solution dramatically improves model performance, even in scenarios with initially sparse datasets. Let’s be there to discover how this method could apply to your work.