While deep learning is effective in domains like computer vision, natural language processing, and recommenders, there are areas where its use isn’t mainstream. Tabular data problems, which consist of columns of categorical and continuous variables, commonly make use of techniques like XGBoost, gradient boosting, or linear models. RAPIDS streamlines preprocessing of tabular data on GPUs and provides a seamless handoff of data directly to any frameworks supporting DLPack, like PyTorch, TensorFlow, and MxNet. These integrations open up new opportunities for creating rich workflows, even those previously out of reason like feeding new features created from deep learning frameworks back into machine learning algorithms.