Online deployed services refer to the use of cloud computing to deploy an application or service over the internet. In Azure, this could involve deploying Machine Learning models as web services, which can then be consumed or used by others. The deployed service could be a predictive service, a classification service, a recommendation service, etc.
Importance of Testing Deployed Services
Testing a deployed service confirms that it performs as expected after it’s been deployed. It confirms the following:
- Performance Testing: This checks the system’s behavior under a particular load, often at peak usage times.
- Integration Testing: This ensures that the service works well with other parts of the system.
- Security Testing: This ensures that the system, data, and resources are protected from threats.
- Functional Testing: This checks the system’s functional requirements.
Steps to Test an Online Deployed Service
1. Deploy Your Model as a Service
The first step in testing an online deployed service is, of course, to deploy a service. On Azure, you can use the Azure Machine Learning studio/web interface, or use the Azure Machine Learning SDK to write scripts for deploying models.
2. Create a Test Environment
You need to create a similar test environment with identical configurations as your production environment for testing purposes. This includes making sure that any required resources, such as databases or queues, are available.
3. Generate Test Data
Test data should represent a similar distribution as real production data. This will help you accurately measure your model’s performance and simulate real-life scenarios.
4. Test the Deployed Service
Now that your service is deployed, your test environment is set up, and you have your test data, you can test your service.
Here’s an example of how you may test a deployed service in Python:
import requests
import json
# URL for the web service
scoring_uri = 'http://
# If the service is authenticated, set the key
headers = {'Content-Type':'application/json', 'Authorization':('Bearer '+ '
test_data = json.dumps({"data": [
[1,2,3,4,5,6,7,8,9,10],
[10,9,8,7,6,5,4,3,2,1]
]})
response = requests.post(scoring_uri, data=test_data, headers=headers)
print("Response Code:", response.status_code)
print("Predicted Values:", response.text)
This example uses the requests library in Python to send POST requests to the online deployed service. You pass in the test data as a JSON string, and you get a prediction from the online service.
5. Analyze Test Results
Finally, you need to analyze the results from the test to ensure your service is functioning as expected. This typically involves comparing the results against a known or expected output.
Conclusion
Testing an online deployed service is a key part of implementing any data science solution, especially in the Azure environment. It ensures your service is functioning correctly, robustly, and securely once deployed. Demonstrating an understanding of this process is likely to be valuable when preparing for the DP-100 exam. Remember to not only focus on the technical skills but also understand why each step is essential in the process.
Practice Test
True or false: It is not possible to monitor and evaluate an online deployed web service in Azure.
- True
- False
Answer: False
Explanation: Azure provides several tools and features to monitor and evaluate the performance of a deployed web service, for example, Application insights.
Azure Pipelines is used to _____________.
- a) Build, test and deploy applications
- b) Analyze data
- c) Store data
- d) Analyze deployment
Answer: a) Build, test and deploy applications
Explanation: Azure Pipelines is a cloud service that can be used to automatically build, test, and deploy your code project.
Multiple Select: What are the three key components of the Azure model to monitor, diagnose, and gain insight to live deployed applications?
- a) Application Insights
- b) Azure Monitor
- c) Azure Analysis Services
- d) Log Analytics
Answer: a) Application Insights, b) Azure Monitor, d) Log Analytics
Explanation: Azure uses Application Insights, Azure Monitor, and Log Analytics to diagnose and gain insights to live applications.
Which of these is not a step in designing and implementing a data science solution on Azure?
- a) Building python code
- b) Planning and designing a solution
- c) Implementing and running a solution
- d) Testing and validating the deployment
Answer: a) Building python code
Explanation: While Python is often used in the process, building Python code is not a specific step in the process of designing and implementing a data science solution on Azure.
True or False: For Azure deployed services, the uptime is guaranteed 100% of the time.
- True
- False
Answer: False
Explanation: Although Azure makes every effort to ensure their services are available as much as possible, they do not guarantee 100% uptime.
Single Select: Azure _________ helps in identifying underperforming microservices.
- a) Monitor
- b) Pipelines
- c) Portal
- d) Functions
Answer: a) Monitor
Explanation: Azure Monitor can collect data from various sources to provide insights about the performance and availability of applications, workloads, and infrastructure.
True or False: Azure Machine Learning can be used to deploy, manage and monitor machine learning models.
- True
- False
Answer: True
Explanation: Azure Machine Learning provides a cloud-based environment you can use to develop, train, test, deploy, manage, and track machine learning models.
In Azure, iterative testing and deployment of models is possible through _______.
- a) Power BI
- b) Azure Databricks
- c) Azure ML pipelines
- d) Azure Storage
Answer: c) Azure ML pipelines
Explanation: Azure Machine Learning pipelines allow for iterative testing and deployment of models making the process efficient.
True or False: Microservices cannot be easily scaled in Azure.
- True
- False
Answer: False
Explanation: Azure has the capability to automatically scale microservices based on the demand and need.
Single Select: Which of the following helps to diagnose networking issues in Azure?
- a) Azure Advisor
- b) Azure DNS
- c) Network Watcher
- d) Azure Backup
Answer: c) Network Watcher
Explanation: The Azure Network Watcher is a service that provides tools to monitor, diagnose, view metrics, and enable or disable logs for resources in an Azure virtual network.
Interview Questions
What is Azure DevOps?
Azure DevOps is a Microsoft product that provides a range of development tools for planning work, collaborating on code development, and deploying applications.
How do you manage an Azure ML workspace using the Azure portal?
An Azure ML workspace can be managed using the Azure portal by navigating to the dashboard of your portal, selecting ‘All services’ and entering “Machine Learning” in the search box. After selecting it, you can see a list of all workspaces attached to your Azure subscription.
Where would you configure the compute target in Azure ML studio for deploying a model?
The compute target can be configured in Azure ML Studio under the ‘Compute’ tab in the left menu, where you can select ‘Compute Clusters’ and then ‘New’ to create one.
What are the steps in using Azure Machine Learning designer?
The steps to use Azure Machine Learning designer are creating a pipeline, adding and connecting dataset and modules, running the pipeline, and deploying the model.
How can you monitor a deployed service in Azure?
Azure Monitor provides a robust set of tools for monitoring a deployed service. This includes logging and analysis tools, metrics and alerts, and more.
What is Azure Kubernetes Service?
Azure Kubernetes Service (AKS) is a managed container orchestration service provided by Azure for easily deploying, managing, and scaling applications.
What steps should you take to troubleshoot a failed deployment in Azure?
The steps to troubleshoot a failed deployment in Azure are first to understand the error message, inspect deployment operations and events, fix the error based on information from these sources, and finally re-deploy.
What is a REST endpoint in the context of machine learning deployment in Azure?
A REST endpoint is an URL. Once a model is deployed in Azure ML Studio, it provides a REST endpoint that can be used to integrate the model with any application.
What are the two key types of Web Service deployments offered by Azure Machine Learning (AML)?
The two key types of Web Service deployments offered by AML are Azure Kubernetes Service (AKS) and Azure Container Instance (ACI).
What is Azure Container Instance?
Azure Container Instance (ACI) is a service that allows you to run containers directly on Azure’s managed infrastructure, without needing to provision or manage any underlying infrastructure.
How do you manage application settings or connection strings for deployed models in Azure?
Application settings and connection strings for deployed models in Azure can be managed through App Service settings in the Azure portal.
What is “MLOps” and how does it apply to Azure?
MLOps, or DevOps for machine learning, is the practice of combining machine learning, operations, and development to deliver efficient and reliable machine learning solutions. In Azure, MLOps capabilities enable more reproducible, auditable and easier-to-maintain ML workflows.
What is Azure ML SDK?
Azure Machine Learning SDK is a set of Python packages designed for machine learning tasks in the Azure environment. This includes tasks such as model training, model deployment, and more.
What is the use of Deployment Configuration in Azure ML?
A deployment configuration in Azure ML includes specifications for the compute resources to allocate for a model deployment, including CPU, memory, and the target compute platform.
How to scale a deployed service in Azure?
Deployed services in Azure can be scaled either manually or automatically (autoscale) using the Azure portal, Azure CLI, PowerShell, or Azure Resource Manager Template.
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