Analyzing the captured data

Of course, there are other ways to process the data that’s been captured and stored inside the S3 bucket. Instead of using the built-in model monitoring capabilities and features discussed in the previous section, we can also download the collected ML inference endpoint data from the S3 bucket and analyze it directly in a notebook.

Note

It is still recommended to utilize the built-in model monitoring capabilities and features of SageMaker. However, knowing this approach would help us troubleshoot any issues we may encounter while using and running the automated solutions available in SageMaker.

Follow these steps to use a variety of Python libraries to process, clean, and analyze the collected ML inference data in S3:

1. Create a new Notebook by clicking the File menu and choosing Notebook from the list of options under the New submenu.

Note

Note that we will be creating the new notebook inside the CH08 directory beside the...