You work for an online retailer as a machine learning specialist. Your team is building a deep learning model based on the Keras Sequential model to categorize the clothing your company’s users post on their Instagram feeds when they use one of the hashtags that refer to your company. You are the machine learning specialist assigned to building the training run visualization code to allow the team to monitor the training metrics of the model as it trains.
How would you go about visualizing the training metrics? (Select TWO)

Answer options:

A.When creating your model training job in the SageMaker console, specify a regex pattern for the metrics that you want your model training script to write to your logs.
B.When creating your model training job in the SageMaker console, specify the metrics that you want your model training script to write to your logs.
C.Use the CloudWatch metrics dashboard to visualize the metrics that SageMaker automatically parsed from your logs and published for graphing and visualization.
D.Use the SageMaker metrics dashboard to visualize the metrics that SageMaker automatically parsed from your logs and published for graphing and visualization.
E.Write a python script in your SageMaker jupyter notebook to visualize the metrics that SageMaker automatically parsed from your logs and published for graphing and visualization.

Answers: A, C
Option A is correct. While creating your model training job in the SageMaker console, you specify a regex pattern used for the metrics that your model training script writes to your logs.
Option B is incorrect. While creating your model training job in the SageMaker console, you specify a regex pattern used for the metrics that your model training script writes to your logs. You can’t specify the metrics directly. You must use a regex pattern.
Option C is correct. SageMaker parses from your logs the metrics which you wish to track and publishes them to CloudWatch. The CloudWatch metrics dashboard allows you to visualize your SageMaker training job metrics as graphs for visualization.
Option D is incorrect. The CloudWatch metrics dashboard allows you to visualize your SageMaker training job metrics as graphs for visualization, not the SageMaker metrics dashboard.
Option E is incorrect. You would not need to write a python script to visualize your metrics data since the CloudWatch metrics dashboard gives you this functionality.
Reference:
Please see the AWS Machine Learning Blog titled Easily monitor and visualize metrics while training models on Amazon SageMaker, the Amazon SageMaker developer guide titled Use TensorFlow with Amazon SageMaker, and the Tensorflow.org page titled Basic classification: Classify images of clothing.