From the course: Amazon Web Services Machine Learning Essential Training
Unlock the full course today
Join today to access over 22,400 courses taught by industry experts or purchase this course individually.
Work with EMR for machine learning - Amazon Web Services (AWS) Tutorial
From the course: Amazon Web Services Machine Learning Essential Training
Work with EMR for machine learning
- [Narrator] So the next service we're going to look at is Elastic MapReduce, which is managed Hadoop, Spark, and other type of library clusters of virtual machines. So, the question is, why should we use virtual servers when we have API's, docker containers, and many other options? Well, the answer is, you shouldn't always. But there are situations for which you need the level of control. Could be security requirements. Could be custom setup steps. Could be the amount of data. I've been working with some bioinformatics customers, and in processing genomic sequencing result output, the data is huge, and taking advantage of the economies of spot pricing on EC2 is really critical for some machine learning workloads. Amazon Elastic MapReduce is platform as a service. It's Hadoop clusters, so master and worker nodes, that are customized EC2 instances, that are designed to run Hadoop and its associated libraries, such as Spark, SparkML, or machine learning, and other workloads. Many data…
Practice while you learn with exercise files
Download the files the instructor uses to teach the course. Follow along and learn by watching, listening and practicing.
Contents
-
-
-
-
-
-
Understanding ML virtual servers4m 7s
-
Understanding deep learning2m 36s
-
Work with Gluon for MXNet in SageMaker5m 14s
-
Work with MXNet in SageMaker9m 1s
-
Databricks on AWS7m 2s
-
Work with MXNet in Databricks9m 2s
-
Set up the AWS Deep Learning AMIs6m 38s
-
Work with the AWS Deep Learning AMI4m 16s
-
Work with EMR for machine learning8m 40s
-
-
-