You Will Learn
-How to implement modern real time ML solution based on Redis-ML and Spark ML
-How we built an end-to-end prototype for a recommendation engine that is similar to what Netflix does on Databricks
-How to easily deploy models into production with Databricks and Redis Labs
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.
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