We are now, simply, Redis
Predictive intelligence from machine learning (ML) has the potential to change everything in our day to day experiences, from education to entertainment, from travel to healthcare, from business to leisure and everything in between.
Modern ML frameworks are designed to train ML models, but were not designed to serve these models in production. Many modern ML scenarios these days require the flexibility of changing parts of the served model in real-time, without going through a complete training cycle.
Doing this while retaining accuracy, is a real-time challenge. Homegrown solutions typically require tens or hundreds of servers and complex deployment processes.
Join us and Databricks, the creators of Apache Spark, to learn how running a trained model in production can be substantially accelerated and radically simplified by using Redis-ML as a serving layer for modern ML models created by Spark ML. We will demonstrate how to build an end-to-end prototype for a recommendation engine similar to that of Netflix on the Databricks virtual analytics platform and highlight how Databricks can help you quickly build, train, and deploy ML models.
|When:||March 21st, 2017 | 9:00 am|
|Featured Speaker:||Shay Nativ, Software Developer, Redis|
Richard Garris, Principal Solutions Architect, Databricks Inc.
|Audience:||Redis and NoSQL Users|
Shay is an experienced software developer, architect, and entrepreneur. He was the founder and VP R&D of Peak-Dynamics—an energy saving solution for water utilities and CTO at Utab, a web platform for musicians. Shay loves solving complex problems and writing performant code.
Richard Garris is a Principal Solutions Architect at Databricks focused on helping clients with their Advanced Analytics initiatives using Apache Spark and MLlib . He has spent 13 years working with enterprises in data management and analytics. Richard got his undergraduate degree at The Ohio State University and Masters in Software Management from CMU. His previous work experience includes Skytree, Google and PwC.