Amazon AI/ML Stack
Image Courtesy: AWS Docs
Amazon’s Web Services have a series of optimized services specifically tailored for Artificial Intelligence and Machine Learning Algorithms. These fit into three major tiers, as follows:
Amazon SageMaker
Now that we know where Amazon’s SageMaker Service falls, lets delve a bit deeper into it.
A generic Machine Learning Pipeline has the following primary modules:
SageMaker combines these modules and works with three major components:
These components are independent of each other and can be used separately or even in required combinations.
The management of SageMaker components is extremely easy through Amazon SageMaker Console which has a very clean layout, making options for the different components easily accessible and configurable.
BUILD
The build phase initiates the first interaction of Data with the Pipeline. The easiest way to do this is to generate SageMaker’s Notebook Instances. This not only enables the integration of the required code but also facilitates clear documentation and visualization.
The other options available for code integration is Spark SDK which enables integration of Spark pipeline through AWS’s Elastic Map Reduce or EMR service.
TRAIN
Setting up the training module in SageMaker is extremely easy and feasible. The primary attractions of the training component in SageMaker are as follows:
DEPLOY
There are several deployment options in SageMaker. With SageMaker’s UI, it is a one-step deployment process, providing high reliability with respect to quality, scalability and high throughput facilities.
Several models can be deployed using the same end-point (the point of deployment) so that the model can go through A/B testing which is supported by SageMaker.
One major advantage of the deployment facility is that SageMaker allows upgrades, updates and other modifications with zero downtime, owing to blue-green deployment (when two similar production environments are live such that if one goes down, the other one keeps the server up and running).
Batch predictions, which are often required in production, can also be carried out using SageMaker with specific instances which would stream data in from and out f S3 and distribute the tasks among GPU or CPU instances (as per the configuration).
ADDITIONAL FUNCTIONAL LAYERS
With this, we have come to the end of Amazon SageMaker basic concepts. Watch this section for a DEMO on how to get started with SageMaker, which will be published soon.
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Previously published at http://machinelearningtub.blogspot.com/2020/04/amazon-ai-and-ml-stack-amazons-web.html