This podcast explores how graph embeddings enhance machine learning models by capturing relationships and context that traditional tabular data often misses. It discusses representation learning, feature engineering, graph data science techniques, and real-world applications where graph embeddings significantly improve predictive accuracy. Perfect for data scientists, machine learning engineers, AI practitioners, and analytics professionals looking to unlock deeper insights from connected data.