Completing the end-to-end ML pipeline
In this section, we will build on top of the (partial) pipeline we prepared in the Running our first pipeline with SageMaker Pipelines section of this chapter. In addition to the steps and resources used to build our partial pipeline, we will also utilize the Lambda functions we created (in the Creating Lambda functions for deployment section) to complete our ML pipeline.
Defining and preparing the complete ML pipeline
The second pipeline we will prepare would be slightly longer than the first pipeline. To help us visualize how our second ML pipeline using SageMaker Pipelines will look like, let’s quickly check Figure 11.16:
Figure 11.16 – Our second ML pipeline using SageMaker Pipelines
Here, we can see that our pipeline accepts two input parameters—the input dataset and the endpoint name. When the pipeline runs, the input dataset is first split into training, validation, and test sets. The...