Using Apache Spark with Amazon SageMaker


Using Apache Spark with Amazon SageMaker

Broadcast Date:
May 22, 2018

Level 300 | Service Deep Dive
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker also provides an Apache Spark library, in both Python and Scala, that you can use to easily train models in Amazon SageMaker from your Spark clusters. Once a model has been trained, you can also deploy it using Amazon SageMaker hosting services. After a brief recap on Amazon SageMaker, this code-level webinar will show you how to integrate your Apache Spark application, including how to start training jobs, integrate them in Spark pipelines, and more.

Learning Objectives:
• Learn more about the Apache Spark library that can be used with Amazon SageMaker to train models from your Spark clusters
• Get exposed to code that demonstrates how you can integrate your Spark application with Amazon SageMaker
• Learn how to start training jobs from Apache Spark and integrate training jobs in Spark pipelines

Suited For: Machine Learning Developers, Deep Learning Developers, Data Scientists, Amazon SageMaker customers, Apache Spark Developers

Speaker(s): Julien Simon, Principal Evangelist, AWS

Having trouble with this page? Please email us at

Download the Slide Deck