ON-DEMAND

Large Scale Machine Learning with Spark on EMR

Broadcast Date: July 23, 2018

Level 300 | Service Deep Dive

Building machine learning models on large amounts of data is challenging and so is applying trained models on big data. By using Spark, MXNet, TensorFlow, and other frameworks on EMR, customers can build ML models using distributed training on large amounts of data and perform distributed inference. In this tech talk, we dive deep into how to use Spark and ML/DL frameworks such as SparkML, TensorFlow, and MXNet.

Learning Objectives:
• Learn how Amazon EMR can be used for distributed training and inference on big data
• Learn how to do distributed training and inference on Amazon EMR using popular ML/DL frameworks
• Learn how to deploy machine learning models on Amazon EMR for large scale distributed inference

Suited For: Anyone interested in Amazon EMR

Speaker(s): Tom Zeng, Amazon EMR Solutions Architect, AWS

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