Deep learning algorithms have benefited greatly from the recent performance gains of GPUs. However, it has been unclear whether GPUs can speed up data manipulations such as joins and aggregations and machine learning algorithms such as generalized linear modeling, random forests, gradient boosting machines, and clustering. H2O.ai, the leading open source AI company, is bringing the best-of-breed data science and machine learning algorithms to GPUs, not just deep learning. In addition, H2O.ai is porting data.table to GPUs, already the fastest open-source columnar data frame library and the world's fastest implementation of the sort algorithm. This powerful combination will enable the fastest data science and machine learning pipelines for AI transformations for applications such as IoT time series, fraud prevention, anomaly detection, and many more. We'll demonstrate benchmarks for the most common algorithms relevant to enterprise AI and showcase performance gains as compared to running on CPUs.