AWS Sagemaker and R

Introduction

I’m excited to announce my sagemaker R package!

AWS Sagemaker is a powerful tool, and I hope my package makes it easier for people to try it out!

Since the Github page and website already introduce the sagemaker R package, I want to use this blog post to introduce AWS Sagemaker, productionizing machine learning, and how the my sagemaker R package tries to make it all easier.

AWS Sagemaker

AWS Sagemaker is a platform for training machine learning models.

There are three components of Sagemaker:

1. hosted development environment with juypter notebooks
2. scalable training of machine learning models
3. endpoint and batch predictions from trained models

Hosted juypter notebooks are a great feature, but this post will focus on Sagemaker’s scalable training and predictions.

Productionizing machine learning predictions

Before we dive into AWS Sagemaker, let me introduce my mental model for generating machine learning predictions:

The workflow on the bottom is similar to the data exploration workflow shown in R for Data Science. We process data, evaluate, and select a model to deploy.

On the top, the key to repeatable, scheduled predictions is the predictions pipeline environment. A mature environment might be a docker image ran by AWS Batch, scheduled by a CRON trigger on Jenkins. A simple one would be an Rscript ran once a day on your desktop.

Think of the pipeline like a function, deployed on a certain trigger:

## 🕐 
## <clock for 13:00 (~one o’clock) >

pipeline <- function(...) {
  data <- fetch_data(...) %>%
    transform(...)
  
  model <- sagemaker::sagemaker_attach_tuner(...)
  predict(model, data)
}

One key feature of the environment is that processes are shared with the training workflow.

For example, any data transformation or feature engineering process must be shared between the training workflow and the predictions pipeline. Otherwise, your predictions will not be reliable, and your model might not even run against the new data if its a different shape.

Likewise, the pipeline needs to use the model we selected during training.

Sagemaker Features

What Sagemaker isn’t

Sagemaker is not a full end-to-end solution for productionizing machine learning predictions. Ultimately, Sagemaker will only act as an API service to generate predictions.

That means you’ll need to find other services to build and maintain the entire predictions pipeline environment. This is a complex system, which includes:

• the environment itself, usually a docker image
• scheduling and triggering the environment
• reusing components from the model training, like data transformation and feature engineering.

Sagemaker is a great tool for machine learning, but it won’t take you all the way.

sagemaker R package

So how does the new sagemaker R package fit into this?² First and foremost, the sagemaker R package is an interface to the AWS Sagemaker API. This means that it’s easier to build a predictions pipeline environment using R.

You can do all your data maniuplation and cleaning from R, as well as manage your Sagemaker APIs.

Second, I think this R package vastly simplifies the Sagemaker interface, especially during model training and evaluation. It’s easier to compare hyperparameters across tuning jobs, and make predictions on new data.

Most importantly, I’ve tried to hide a lot of the details that get in the way when you are trying to quickly spin up a Sagemaker model. Less boilerplate, more machine learning.