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 platforms - Amazon Web Services (AWS) Tutorial
From the course: Amazon Web Services Machine Learning Essential Training
Understanding ML platforms
- [Instructor] In this next section, we're going to take a look at platforms that are available when you're selecting AWSML for your particular business scenario. Now, in this drawing, we have a subset of the platforms that are available, and the reason is, as at the services level of AWSML services, there have been a number of new services that have been released within the past 12 months of the time of this recording. Same goes for platforms. In fact, there's so much activity here, that I'm actually splitting this into two sections in this course so that you can select, understand, comprehend, and choose the appropriate platform, if that's the level at which you want to work. To that end, I've separated out the Platform section into two subsections, and in this part of the course, we're going to cover what I call the serverless or the container-directed section of AWS machine learning services. 'Cause these, as you would expect, are mostly the newer services. So, there are three…
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 platforms3m 53s
-
Understanding and using AWS Machine Learning9m 15s
-
Understanding SageMaker3m 54s
-
Create Jupyter notebooks with SageMaker6m 12s
-
Get data with SageMaker notebook6m 25s
-
Train model with SageMaker job3m 6s
-
Deploy and host model with SageMaker model2m 31s
-
Use model from SageMaker endpoint4m 7s
-
Selecting algorithm for model training5m 36s
-
Advanced use of SageMaker2m 37s
-
-
-
-