From the course: Amazon Web Services Machine Learning Essential Training

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Understanding SageMaker

Understanding SageMaker - Amazon Web Services (AWS) Tutorial

From the course: Amazon Web Services Machine Learning Essential Training

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Understanding SageMaker

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- [Instructor] As we get started working with our next service, AWS SageMaker, let's take a look at a typical machine learning model workflow. You can see in this diagram, there are three high-level steps. To generate example data, train a model, and deploy the model, and then there are substeps of fetching, cleaning, and preparing the data, training and evaluating the model, and deploying and monitoring the deployed model. SageMaker has components that fit with each of these parts. SageMaker is a relatively new service at the time of this recording. It's only been in general availability since this last year's reinvent, so three months ago, and it has four general steps. The steps are a component that supports building, so it allows connection to other AWS services, and transforming data using something called a SageMaker notebook. Then training. You can use SageMaker's algorithms and frameworks, or you can use your own for distributed training. Distributed is really key here. The…

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