From the course: Learning Amazon SageMaker
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Deploy trained model
- [Instructor] Now that a model has been successfully trained and saved to the Amazon SageMaker platform, I can show you how simple it is to start hosting that model. Now usually the hosting of a model is done by completely separate data science team, that uses different technologies, but in this case with Amazon SageMaker it's part of the same package, same library. So using the xgb object that we created earlier we'll run the deploy function. We'll deploy the model on one instance and we'll define that instance's size. So in this case an ml.m4.xlarge. So running this function you can see the log output creating the model, creating the endpoint. You can see that the progress bar is clicking along the bottom here. But you can also get updates by going to the Amazon SageMaker web interface. By clicking on endpoints you will see that the status is set to creating, and it is currently going through the process of creating that API endpoint. By clicking on the name you can get updates…
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