From the vantage point of an active CDHI data scientist, we will demonstrate how a highly modern, zero-trust, cloud-native, data science environment can be used for active distribution of complex data science tasks in a heterogenous research compute environment. Expect an introduction and real-world examples of how technologies that include Kubernetes, Jupyter, and Kubeflow can be used to improve data science. Time permitting, we will close by explaining how the use of Kubernetes provides broad-based compute freedom to embrace existing cloud, enterprise, and national resourcing.
Slides: https://ucsf.box.com/s/tyt1nomo5yrbhp3xo5du12v0cx7ephng (MyAccess login required)
An interest in modern computing and data science will suffice.
Lu Chen is a staff data scientist at the Center for Digital Health Innovation developing deep neural network algorithms for medical data. In support of that work, she is actively designing and using project based containerization using Docker.
Pavan Gupta is a staff data engineer at the Center for Digital Health Innovation responsible for architecting and building Cloud and Kubernetes environments including CDHI's Artificial Learning and Intelligence Compute Environment (ALICE), ARS's Wynton-K8s architecture (singularity+K8s), and SOM Tech's earliest iteration of its Amazon Research Cloud.