Push straight to prod API development with R and Tensorflow at TMobile - Heather & Jacqueline Nolis

January 24, 2019

When tasked with creating the first customer-facing machine learning model at T-Mobile, we were faced with a conundrum. We had been told time and time again to deploy machine learning models in production you had to use Python, but our very best data scientists were fluent in building neural networks in R with Keras and TensorFlow. Determined to avoid double work, we decided to use R in production for our machine learning models. After months of work, wrangling our containers to meet cloud security compliance, and conforming to DevOps standards, we succeeded in creating a containerized API solution using the keras and plumber R packages and Docker. Today R is actively powering tools that our customers directly interact with and we have open sourced our methods. In this talk, we'll walk through how to deploy R models as container-based APIs, the struggles and triumphs we've had using R in production, and how you can design your teams to optimize for this sort of innovation.


Jacqueline Nolis 
Dr. Jacqueline Nolis has over a decade of experience in the data science industry, working with companies ranging from DSW and Union bank to Microsoft and Airbnb. Her academic research covered optimization under uncertainty with a specialization in electric vehicle routing, which yielded a PhD in Industrial Engineering from ASU - and provided a solid basis for artificial intelligence, data science, and machine learning. Previously, Jacqueline was the Director of Insights and Analytics at Lenati and a Lead of Advanced Analytics at Promontory Financial Group.
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Heather Nolis 
Heather began her academic career receiving a dual degree in French and Neuroscience with intent to pursue a PhD in molecular neuropharmacology. Once she realized how heavily that field relied on software built by other people, she pivoted - deciding to make software herself. Over her time in graduate school for Computer Science at Seattle University and working as a software engineer at T-Mobile, she's developed significant strength in machine learning, cloud computing, and proof-of-concept product development.

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