From the course: Machine Learning with SageMaker by Pearson

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Real-time inference demonstration

Real-time inference demonstration - Amazon SageMaker Tutorial

From the course: Machine Learning with SageMaker by Pearson

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Real-time inference demonstration

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There are several examples in this course of how to train and deploy a model to an endpoint in SageMaker. The majority of those, maybe all of them, have been done programmatically, either with Canvas low-code or with a Jupyter Notebook and using the Boto3 SDK. SDK. I'm going to give you one more example, and that is doing it with the SageMaker web console. So I have the instructions laid out here. We're going to use that loan approval model, and we're going to deploy it to an endpoint. This is incorrect. This is going to be, let me go ahead and fix this real time. This is going to be text CSV, and we'll do something like like 20 years old with a high school education, and we'll say 88332. It's going to be the data set that we're going to pass in. Then we'll invoke it using Python. Interpret the response, monitor, delete the endpoint. So this one should be relatively quick. Let's hop into SageMaker AI in the web console. Duplicate this tab and head over to S3, because we need to get…

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