You work on an application development team for a new start-up social media site. Your team is made up of data scientists and machine learning specialists, of which you are the lead machine learning specialist. Your team has built a model in SageMaker using the built-in linear learner algorithm. The team has performed several training runs to find the best datasets and hyperparameters. You have decided to use the SageMaker model tracking capability to manage the many training runs your team has produced.
You have asked your team to show you the results of their efforts to help you lead them in deciding which hyperparameters and test datasets to use. They have used the AWS SDK API for SageMaker to produce the data for your decision. The following is a section of code from their use of the SageMaker model tracking capability. What does the code do?
search_params = {
“MaxResults”: 10,
“Resource”: “TrainingJob”,
“SearchExpression”: {
“Filters”: [{
“Name”: “Tags.Model”,
“Operator”: “Equals”,
“Value”: ”Model_Social_Media_Classifier”,
}]},
“SortBy”: “”Metrics.train:precision”,
“SortOrder”: “Descending”
}
smclient = boto3.client(service_name=’sagemaker’)
results = smclient.search(**search_params)

Answer options:

A.It uses the SageMaker API to run at most 10 training jobs for a model called Model_Social_Media_Classifier and sorts the results by the model precision in descending order.
B.It uses the SageMaker API to find the 10 best hyperparameters (based on the precision metric) of a model that has been tagged as Model: Model_Social_Media_Classifier.
C.It uses the SageMaker API to find the 10 best training runs (based on their precision metric) of a model that has been tagged as Model: Model_Social_Media_Classifier.
D.It uses the SageMaker API to run a training job called Model_Social_Media_Classifier and sorts the results by the precision metric in descending order for the 10 best results.

Answer: C
Option A is incorrect. The code uses the SageMaker python client API to search your team’s SageMaker resources (such as training run results) for a specific model’s training run results. It does not run any training jobs.
Option B is incorrect. The code uses the SageMaker python client API to search your team’s SageMaker resources (such as training run results) for a specific model’s training run results. It does not search for the best hyperparameters.
Option C is correct. The code uses the SageMaker python client API to search your team’s SageMaker resources (such as training run results) for a specific model’s training run results. It then sorts the results by the precision metric in descending order. This will allow you to see which training model run performed the best from a precision perspective. The results give that model’s algorithm, data sources, hyperparameter values, and metrics results.
Option D is incorrect. The code uses the SageMaker python client API to search your team’s SageMaker resources (such as training run results) for a specific model’s training run results. It does not run any training jobs.
Reference:
Please see the AWS announcement titled New Model Tracking Capabilities for Amazon SageMaker Are Now Generally Available, the AWS Machine Learning blog titled Using model attributes to track your training runs on Amazon SageMaker, the Amazon SageMaker developer guide titled Search, the Amazon SageMaker developer guide titled Manage Machine Learning Experiments, the AWS SageMaker Client boto3 docs, and the Amazon SageMaker developer guide titled Tune a Linear Learner Model.