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.
Understanding ML virtual servers - Amazon Web Services (AWS) Tutorial
From the course: Amazon Web Services Machine Learning Essential Training
Understanding ML virtual servers
- [Narrator] In this next section, we're going to address the question, "Should I use a server, virtual machine or, actually, a server cluster for machine learning?" It's really a key question and it's quite timely as the vendors, including Amazon, are upgrading their services to reflect changes in underlying compute options. What do I mean by that? Well, I started the course not with servers, which would be typical for machine learning a couple of years ago, rather with APIs. We looked at just working with endpoints and being charged by the service call for things like automatic image labeling and text-to-speech translation, common machine learning tasks these days. Next, I looked at SageMaker, which is a scalable set of componentized services that you can use in any combination to suit you. Of course, there still is a lot of server-based machine learning work out there, but my point is to get you thinking and to consider using some of the newer options because they can allow for a…
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
-
-
-