DP-100 Exam Dumps : Designing and Implementing a Data Science Solution on Azure

Step 1: Sweep Clustering

Start by using the "Tune Model Hyperparameters" module to select the best sets of parameters for each of the models we're considering.

One of the interesting things about the "Tune Model Hyperparameters" module is that it not only outputs the results from the Tuning, it also outputs the Trained Model.

Step 2: Train Model

Step 3: Evaluate Model

Scenario: You need to provide the test results to the Fabrikam Residences team. You create data visualizations to aid in presenting the results.

You must produce a Receiver Operating Characteristic (ROC) curve to conduct a diagnostic test evaluation of the model. You need to select appropriate methods for producing the ROC curve in Azure Machine Learning Studio to compare the Two-Class Decision Forest and the Two-Class Decision Jungle modules with one another.

Box 1: from azureml.core.webservice import Webservice

The following code shows how to use the SDK to update the model, environment, and entry script for a web service to Azure Container Instances:

from azureml.core import Environment

from azureml.core.webservice import Webservice

from azureml.core.model import Model, InferenceConfig

Box 2: predictions = service.run(input_json)

Example: The following code demonstrates sending data to the service:

import json

test_sample = json.dumps({'data': [

[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],

[10, 9, 8, 7, 6, 5, 4, 3, 2, 1]

]})

test_sample = bytes(test_sample, encoding='utf8')

prediction = service.run(input_data=test_sample)

print(prediction)

Box 1: Attached compute

Box 2: Inference cluster

Box 3: Training cluster

Box 4: Attached compute