Empower Your Data Analysts With Self-Service Application Deployment Using Panel
Rapid development and deployment of ML and data science applications empowers individual analysts and data scientists to put applications directly into production, generating tremendous value for an organization. We will briefly cover the process of rapidly developing an application in Python using the open-source Panel and Lumen libraries from Anaconda’s HoloViz group. Next we will discuss the infrastructure required to make self-service deployment possible, using one of the largest financial institutions as a case study and looking at a number of deployed applications. Lastly we will demonstrate how analysts can exploit many of the features included in Panel, including out of the box database integrations, authentication using external or internal providers like Okta, persisting user state, deep linking, and much more.
A long-term veteran at Anaconda Inc., Philipp Rudiger is a Senior Software Engineer developing open-source and client-specific solutions for data management, visualization and analysis. He is the author of the open source dashboarding and visualization libraries Panel, hvPlot, and GeoViews and one of the core developers of Bokeh, Datashader and HoloViews. Before making the switch to software development he completed a PhD and Masters in Computational Neuroscience at the University of Edinburgh working on biologically inspired, deep and recurrent neural network models of the visual system.