Machine Learning Model Deployment: Strategy to Implementation

Machine Learning Model Deployment: Strategy to Implementation

Thursday, May 23
4:00 PM - 4:40 PM
Dogwood

This talk will introduce participants to the theory and practice of machine learning in production. The talk will begin with an intro on machine learning models and data science systems and then discuss data pipelines, containerization, real-time vs. batch processing, change management and versioning.

As part of this talk, an audience will learn more about:
• How data scientists can have the complete self-service capability to rapidly build, train, and deploy machine learning models.
• How organizations can accelerate machine learning from research to production while preserving the flexibility and agility of data scientists and modern business use cases demand.

A small demo will showcase how to rapidly build, train, and deploy machine learning models in R, python, and Spark, and continue with a discussion of API services, RESTful wrappers/Docker, PMML/PFA, Onyx, SQLServer embedded models, and
lambda functions.

Presentation Video

SPEAKERS

Justin Norman
Director, Research and Data Science Services
Cloudera Fast Forward Labs
Justin leads Cloudera's Fast Forward Labs team. Justin is a career data professional and Data Science leader with experience in multiple industries and companies. Previously, Justin was the head of Applied Machine Learning at Fitbit, the head of Cisco’s Enterprise Data Science Office and a Big Data Systems Engineer with Booz Allen Hamilton after serving as a Marine Corps Officer, with a focus in Systems Analytics and Device Intelligence. Justin is a graduate of the US Naval Academy with a degree in Computer Science and the University of Southern California with a Master’s Degree in Business Administration and Business Analytics.