In the course of just a few years, Graph Neural Networks (GNNs) have emerged as the prominent supervised learning approach that brings the power of deep representation learning to graph and relational data. An ever-growing body of research shows that GNNs achieve state-of-the-art performance for problems such as link prediction, fraud detection, target-ligand binding activity prediction, knowledge-graph completion, and product recommendations. As a result, GNNs are quickly moving from the realm of academic research involving small graphs to powering commercial applications and very large graphs. In this talk we will provide an overview of our work to address the needs of commercial applications, which includes improving the computational efficiency and scaling of GNN model training for extremely large graphs and making it easy for developers to train and use GNN-based models by integrating graph-based ML techniques in graph databases.