This presentation covers the work done at T-Mobile to use R and Keras to develop machine learning models for natural language processing. It show how the successful deployment of R has changed the customer experience.
After having R in production for a year at T-Mobile, the R models were being hit a million time a day. This talks covers the lessons learned from scaling R up in production in an enterprise setting.
Successful machine learning models are built on the foundation of large volumes of high-quality training data. Amazon SageMaker Ground Truth significantly reduces the effort required to create datasets for training. Learn how companies like T-Mobile are using Amazon SageMaker Ground Truth.
This repository contains R code, Dockerfiles, and configurations needed to run your own neural network as an API with HTTPS protocol.
This R package helps you test your APIs to make sure they can handle many users at once. Run a load test with one line of code!
This blog post covers how to use the plumber package of R to create a RESTful API. With this, you can use R code to start a microservice allowing other people to call your R code as needed.
The second blog post of the series works through how to take an web service created in R with plumber and put it in a Docker container.
The final blog post discusses the T-Mobile open source Docker container that contains all the things you need to train a neural network and to deploy it in an enterprise environment.
Machine learning models can be used in small ways to provide simple victories for companies that can quickly produce results with lower risks and lower costs compared to large ML projects.