Boston Housing Example

Now that you've had some time to try and build models using SageMaker, we are going to learn how to deploy those models so that our models can be interacted with using an endpoint.

Inside of the Tutorials folder is the Boston Housing - XGBoost (Deploy) - High Level.ipynb notebook which we will be looking at in the video below.

Using the high level approach makes deploying a trained model pretty straightforward. All we need to do is call the deploy method and SageMaker takes care of the rest.

Similarly, sending data to the deployed endpoint and capturing the resulting inference is easy too as SageMaker wraps everything up into the predict method, provided we make sure that our data is serialized correctly. In our case, serializing means converting the data structure we wish to send to our endpoint into a string, something that can be transferred using HTTP.

In the next video we'll take a more in-depth look at how our model is being deployed.

WARNING - SHUT DOWN YOUR DEPLOYED ENDPOINT

Sorry for the yelling, but this is pretty important. The cost of a deployed endpoint is based on the length of time that it is running. This means that if you aren't using an endpoint you really need to shut it down.