Monitoring a SageMaker endpoint using CloudWatch

Being an end-to-end service for machine learning, SageMaker is one of the main tools that we use to implement various steps of a DL project. In this section, we will describe the last missing piece: monitoring an endpoint created with SageMaker. First, we will explain how you can set up CloudWatch-based monitoring for training where metrics are reported in batches offline. Next, we will discuss how to monitor a live endpoint. 

The code snippets in this section are designed to run on SageMaker Studio. Therefore, we first need to define an AWS Identity and Access Management (IAM) role and a session object. Let’s have a look at the first code snippet:

import sagemaker
# IAM role of the notebook
role_exec=sagemaker.get_execution_role()
# a sagemaker session object
sag_sess=sagemaker.session()

In the preceding code snippet, the get_execution_role function provides the IAM role for the notebook. role_exec. sagemaker.session...