If you've ever struggled with deploying R code and scripts into production, this Meetup is something you won't want to miss.
Abstract and bio are below. Big thanks to Servian for sponsoring the venue, food and drinks.
R is a statistical computing environment and (increasingly) this also includes automated data pipelines, enterprise analytics applications, and web applications. Docker is an open platform that enables users to bundle code with dependencies and run it in multiple environments. When coupled with R it enables users to easily develop R scripts/applications that can be shared on different platforms, deployed in distributed cloud environments and slot into software projects alongside other components.
This talk is modified from a tutorial given at useR!2018. It covers an introduction to DevOps ideas for data science, Docker and some examples to help you get up and running.
This talk will be helpful if you have ever
* Wanted to use an automated script and need to run 100 copies of it at once to process a entire data set.
* Wanted to spin up sandbox data science environments for others (or so you can take your work with you)
* Been stuck for hours installing environment dependencies (e.g. specific C++ or GDAL spatial frameworks)
* Deploy a shiny application to a Windows Server (that doesn’t support Shiny Server).
Elizabeth is in the extremely happy position of having spent her career either:
* Building things with computers, data and maths
* Advocating and communicating about science, maths, and technology
* Leading teams and businesses to build bigger, bolder data science solutions in areas that matter; like environmental management, urban networks and health and human services.
Her background is in computational mathematics but she has provided consulting, analysis and project management to clients in government, environmental management, transport and others. Most of the time she works as Managing Director of Symbolix. They build custom data science and augmented intelligence tools, provide analysis services and support for other data-focussed teams - searching for elegant solutions to wicked problems.