Apache Spark has rapidly become a key tool for data scientists to explore, understand and transform massive datasets and to build and train advanced machine learning models. The question then becomes, how do I deploy these models to a production environment? How do I embed what I have learned into customer facing data applications? In this latest Data Science Central webinar, we will discuss: Best practices on how customers productionize machine learning models Case studies with actual customers Live tutorials of a few example architectures and code in Python, Scala, Java and SQL Speaker: Richard Garris, Principal Solutions Architect -- Databricks Inc. Hosted by: Bill Vorhies, Editorial Director -- Data Science Central

From the recent Spark Summit 2016 in San Francisco, the video presentation below by Joseph K. Bradley of Databricks give focus to "Apache Spark MLlib 2.0 Preview: Data Science and Production." This talk highlights major improvements in Machine Learning (ML) targeted for Apache Spark 2.0. The MLlib 2.0 release focuses on ease of use for data science--both for casual and power users. Finally, the presentation demonstrates these improvements live and show how they facilitate getting started with ML on Spark, customizing implementations, and moving to production. For our reader's convenience, here are the slides for the presentation: