Tague Griffith

Head of Developer Advocacy, Redis Labs, Inc.

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.


Deploying Real-Time Decision Services Using Redis

Most of the energy and attention in machine learning focused on the model training side of the problem. Multiple frameworks, in every language, provide developers with access to a host of data manipulation and training algorithms, but until recently developers had virtually no frameworks to build out predictive engines from trained ML models. Most developers resorted to building custom applications, but building highly available, highly performant applications is difficult. Redis in conjunction with the Redis-ML module provides a server framework for developers to build predictive engines with familiar, off-the-shelf components. Developers can take advantage of all the features of Redis to deliver faster and more reliable prediction engines with less custom development. This talk is a technical session which examines how Redis can be used in conjunction with a Spark based training platform to deliver real-time predictive and decision making features as part of a larger system. To set the context for the session, we start with an introduction to the Redis data model and how features of Redis (namespace, replication) can be used to build fast predictive engines (at scale), that are more reliable, more feature rich and easier to manage than custom applications. From there, we look at the model serving capabilities of Redis-ML and how they can be integrated with a Spark-based ML pipeline to automate the entire model development process from training to deployment. The session ends with a demonstration of a simple machine learning pipeline. Using Spark we train several example models, load them directly into Redis and demonstrate Redis as the predictive engine for making real-time recommendations. At the end of the session, developers should feel confident that they could use Redis as a server framework to build a predictive serving engine for a Spark-based ML pipeline.