This podcast explores how graph embeddings enhance predictive machine learning models by capturing relationships, context, and hidden patterns within connected data. It discusses graph representation learning, feature enrichment, node embeddings, link prediction, and practical applications that improve model performance across industries. Perfect for data scientists, machine learning engineers, AI researchers, and analytics professionals looking to increase predictive accuracy using graph-powered intelligence.