How to Solve Problems with Text Embeddings and ML

Learn to leverage text embeddings and machine learning for practical problem-solving in this 17-minute conference talk from Dallas AI's 2025 Summer School. Discover how text embeddings transform unstructured text data into numerical representations that machines can process effectively, and explore the key differences between using large language models versus embeddings for various applications. Follow along with a hands-on demonstration of ideal customer avatar (ICA) analysis for an AI Builders Cohort, where you'll see how to apply these techniques to real business problems. Gain insights into when to choose embeddings over LLMs, understand the fundamental challenges of working with text data, and explore practical implementation strategies through a complete code example available on GitHub.