Apache Spark™ MLlib 2.x: How to Productionize your Machine Learning Models
On-Demand Webinar
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 model to a production environment? How do I embed what I have learned into customer facing data applications?
In this webinar, we will discuss best practices from Databricks on how our customers productionize machine learning models, do a deep dive with actual customer case studies, and show live tutorials of a few example architectures and code in Python, Scala, Java and SQL.
In this webinar, we will discuss best practices from Databricks on how our customers productionize machine learning models, do a deep dive with actual customer case studies, and show live tutorials of a few example architectures and code in Python, Scala, Java and SQL.
Presenters
Richard Garris
Principal Solutions Architect -Databricks Inc.
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.
Jules S. Damji
Spark Community Evangelist - Databricks
Jules S. Damji is a Apache Spark Community Evangelist with Databricks. He is a hands-on developer with over 15 years of experience and has worked at leading companies building large-scale distributed systems.