Graph neural networks (GNNs) learn from complex graph data and have been remarkably successful in various applications and across industries. Furthering the impact of GNNs entails solving challenges related to modeling and scalability research and productionization. Impactful GNN research requires constant innovation to handle rich, time-evolving, and heterogenous graph data as well as trillion-edge scale graphs. We develop GNN models and distributed training techniques to handle such challenges and integrate those into the deep graph library (DGL). DGL is a scalable and widely adopted library for developing GNN models. Building GNN products requires domain expertise and significant effort. At AWS we aim at lowering the bar in productionizing graph machine learning (GML). Neptune ML facilitates this goal and helps customers obtain real-time GNN predictions with graph databases using graph query languages. At Amazon and AWS we develop frameworks based on DGL to solve internal and external GML problems and realize the impact of GNNs.