Options for deploying R models in production

Question

There doesn\'t seem to be too many options for deploying predictive models in production which is surprising given the explosion in Big Data.

I understand that the

Answer 1

It’s been a few years since the question was originally asked.

For rapid prototyping I would argue the easiest approach currently is to use the Jupyter Kernel Gateway. This allows you to add REST endpoints to any cell in your Jupyter notebook. This works for both R and Python, depending on the kernel you’re using.

This means you can easily call any R or Python code through a web interface. When used in conjunction with Docker it lends itself to a microservices approach to deploying and scaling your application.

Here’s an article that takes you from start to finish to quickly set up your Jupyter Notebook with the Jupyter Kernel Gateway.

Learn to Build Machine Learning Services, Prototype Real Applications, and Deploy your Work to Users

For moving solutions to production the leading approach in 2019 is to use Kubeflow. Kubeflow was created and is maintained by Google, and makes "scaling machine learning (ML) models and deploying them to production as simple as possible."

From their website:

You adapt the configuration to choose the platforms and services that you want to use for each stage of the ML workflow: data preparation, model training, prediction serving, and service management. You can choose to deploy your workloads locally or to a cloud environment.