The rise of GPU-accelerated data science and AI has come about through a combination of open source innovation and better tooling to support reproducible workflows. However, as the diverse array of deep learning libraries continue to mature, attention is moving to other parts of the AI pipeline, including simulation, ETL, and deployment. In this talk, I'll review open source projects that address these other areas, such as Numba, for implementing custom simulations and data transformations on the GPU, and PyGDF, for GPU accelerated dataframes. I'll discuss how the Anaconda Distribution and its conda packaging system helps data scientists create reproducible environments and deploy models. Finally, I'll talk about how Anaconda Enterprise allows data science teams to collaborate efficiently on GPU-accelerated projects with each other, and supports AI workflows from data exploration all the way to deployment.