PAGE 1 | AI @ T-MOBILE Push straight to prod API development with R and TensorFlow at T-Mobile rstudio::conf January 17 th , 2019 Heather Nolis, Machine Learning Engineer, T-Mobile – @heatherklus Jacqueline Nolis, Principal Data Scientist, Nolis LLC – @skyetetra
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PAGE 1 | AI @ T-MOBILE
Push straight to prodAPI development with R and TensorFlow at T-Mobilerstudio::confJanuary 17th, 2019Heather Nolis, Machine Learning Engineer, T-Mobile – @heatherklusJacqueline Nolis, Principal Data Scientist, Nolis LLC – @skyetetra
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What does it mean to put into production?
ANALYSISRunning code once to produce a result
Writing code that is continuously running
BUILD
Putting code into production is letting customers interact with it
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Goal Use machine learning to improve the customer experience
Scope customer care messaging
The project:
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Scenario Customer sends message:“This high bill shall not pass!”
Goal Prep customer care agent before first response: Current bill status
Method• Classify the message with machine learning: [bill breakdown]• Improve the prediction with customer data:
• Recent account activity• Signal strength• Bill status [overdue]
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Model creation with R
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Model building workflow
1. rmarkdown for exploratory analysis
2. Save the model to flat files (and log the build with rmarkdown)
3. Show model off with a shiny demo
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Neural networks with R, Keras, and TensorFlow
A Convolutional Neural Network (CNN) processes initial customer message and customer data
“Unlock my phone.”
ACCOUNT UNLOCK ORDER
0.80 0.150.05
Recent order: YES
CNN
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Model deployment to prod
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Choosing the language
Option Analysis Modeling Deployment Verdict
1 ❎
2 ❎
3 ✅
“If you want machine learning in production you need Python” –literally everyone
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Commit is
made to AI
repository
Jenkins builds the
image
Mesos replicates
and hosts the
containers
Marathon
orchestrates container
deployment
Idea! Treat R like a full programming language(because it is one)