cuML is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects. cuML enables data scientists, researchers, and software engineers to run traditional tabular ML tasks on GPUs without going into the details of CUDA programming. In most cases, cuML's Python API matches the API from scikit-learn.

This tutorial will highlight the most commonly used functionality in cuML like splitting and preprocessing data, training and evaluating a model, and building a machine learning pipeline.