This technical tutorial is designed to address integrating Redis with an Apache Spark deployment to increase the performance of serving complex decision models. The session starts with a quick introduction to Redis and the capabilities Redis provides. It will cover the basic data types provided by Redis and the module system. Using an ad serving use case, Griffith will look at how Redis can improve the performance and reduce the cost of using complex ML-models in production.
You will be guided through the key steps of setting up and integrating Redis with Spark, including how to train a model using Spark and then load and serve it using Redis, as well as how to work with the Spark Redis module. The capabilities of the Redis Machine Learning Module (redis-ml) will also be discussed, focusing primarily on decision trees and regression (linear and logistic) with code examples to demonstrate how to use these features.
By the end of the session, you should feel confident building a prototype/proof-of-concept application using Redis and Spark. You’ll understand how Redis complements Spark, and how to use Redis to serve complex, ML-models with high performance.
Session hashtag: #SFdev14
Tague Griffith is the Head of Developer Advocacy at Redis Labs. At Redis Labs, he focuses on developer education, community growth, and support for the Redis community. Prior to joining Redis Labs, he worked in infrastructure engineering building several high performant Redis Systems.
He holds degrees in Computer Science from Stanford University.