A unified methodology for scheduling workflows, managing data, and offloading to GPUs.
Daniel Whitenack is a Ph.D. trained data scientist/engineer with industry experience developing data science applications for large and small companies, including predictive models, dashboards, recommendation engines, and more. Daniel has spoken at conferences around the world (Gopherfest, GopherCon, and more), maintains the Go kernel for Jupyter, and is actively helping to organize contributions to various open source data science projects.
Best practices and scalable workflows for reproducible data science.
Python and R are widely accepted as logical languages for data science—but what about Go?