New York R Conference 2019

Creating neural networks and putting them in production with R

Deep learning isn't hard, I promise

Jacqueline Nolis, Nolis LLC

Deep learning sounds complicated and difficult, but it’s really not. Thanks to packages like Keras, you can get started with only a few lines of R code. Once you understand the basic concepts, you will able to use deep learning to make AI-generated humorous content! In this talk I’ll give an introduction to deep learning by showing how you can use it to make a model that generates weird pet names like: Shurper, Tunkin Pike, and Jack Odins. If you understand how to make a linear regression in R, you can understand how to create fun deep learning projects.

Rn’t u glad u put R in prod

Heather Nolis, T-Mobile

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.

The code

The good stuff from the talks

This repository contains everything you need to get started with deep learning in R, and putting R code into production as an API. Use the included R script to make a neural network to generate pet names, then use the plumber code to use it as an API and the Dockerfile to create a container.