SageMaker
SageMaker is actually a collection of other products and features all packaged together, it’s an implementation of a fully managed machine learning service. It helps you with the process of developing and using machine learning models.
It includes:
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Data fetching
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Data Cleaning
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Data preparing
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Training models
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Evaluating models
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deploying models
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Monitoring models
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Collecting data
It’s used for this entire machine learning lifecycle.
SageMaker itself has no cost, but the resources that it creates do. And it’s fairly complex pricing because of the range of services which can be created by SageMaker. Because of the complexity of those resources, and because of the high compute requirements of machine learning in general, the resources which are deployed can be relatively large and carry significant cost.
SageMaker Domains
It is an isolation or groupings for a particular project.
Included in a domain are:
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An associated EFS volume
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A list of authorized users
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A variety of security, application, policy, and VPC configurations.
When you start a SageMaker environment, you have to create a SageMaker domain in order to interact with the product.
SageMaker Containers
These are Docker containers deployed to specific machine learning EC2 instances, whose name starts with ml.
These Docker containers are machine learning environments with specific versions of the operating system, libraries, and tooling for the specific task that you’re wanting to accomplish. There are many different pre-built containers that you can utilize with SageMaker.