This podcast explores how graph data science techniques can be used to predict missing or future relationships within large-scale connected datasets. It discusses link prediction algorithms, graph embeddings, feature engineering, machine learning pipelines, and real-world applications in recommendations, fraud detection, social networks, and knowledge graphs. Perfect for data scientists, machine learning engineers, graph practitioners, and analytics professionals looking to apply predictive intelligence to connected data.