Rn’t u glad u put R in prod By Heather Nolis Abstract: Congrats! You built a model that the business wants to run in production! But what even is production? How will your model get there? What potential pitfalls will you hit? How can you advocate for putting your R model in production, instead of rewriting it in a different language? How can you do this all yourself as a data scientist??? In this talk, I will walk through the steps involved in preparing an R model for production using containers (Docker) and container orchestration (Kubernetes) at a big company like T-Mobile or for a side project you desperately want to share with the world in a scalable, fault-tolerant manner. You’ll go from having R code that runs in RStudio your laptop to that same code running safely on a server in the cloud. Bio: Heather began her academic career receiving a dual degree in French and Neuroscience with intent to pursue a PhD in molecular neuropharmacology. Once she realized how heavily that field relied on software built by other people, she pivoted - deciding to make software herself. Over her time in graduate school for Computer Science at Seattle University and working as a software engineer at T-Mobile, she's developed significant strength in machine learning, cloud computing, and proof-of-concept product development. Twitter: @heatherklus Presented at the 2019 New York R Conference (May 11th, 2019)