Creating custom components on Azure is an integral part of designing and implementing a data science solution for the DP-100 Azure certification. Azure Machine Learning (ML) provides you with the tools to build reusable code components. This way, you can encapsulate your data preprocessing steps, model training codes, or any other routine tasks in a workflow. Then, you can include them as steps in your ML pipelines, thereby building efficient, scalable, and fault-tolerant data science solutions.
Let’s get started with the steps required to create custom components.
Step 1: Define the Component
Firstly, you need to create a script that defines the component. This script will contain the logic required for your task, whether it’s data preprocessing, feature extraction, or model training. You should create a function for the tasks you want to execute. Every function should include two sections – signature and implementation:
- Signature: Describes what the function does, and defines the input parameters and output results. Your function’s signature should define at least one output to correctly show as a pipeline step.
- Implementation: Contains the code to perform the task.
Here is an example of a custom Python script defining a component that multiplies two numbers.
def multiply_numbers(x1: int, x2: int) -> pd.DataFrame:
\”\”\”This function multiplies two integers and returns the result as a DataFrame.\”\”\”
result = x1 * x2
return pd.DataFrame(data=[[x1, x2, result]], columns=[‘input1’, ‘input2’, ‘result’])
The function multiply_numbers takes two input parameters, x1 and x2, and returns a pandas DataFrame.
Step 2: Containerize the Component
The next step involves containerizing the function to encapsulate the code and its dependencies. Azure ML SDK’s decorators can be used for this. Let’s continue with our earlier example:
from azureml.pipeline.core.graph import PipelineParameter
from azureml.pipeline.steps import PythonScriptStep
x1_param = PipelineParameter(name=”x1″, default_value=1)
x2_param = PipelineParameter(name=”x2″, default_value=2)
multiply_step = PythonScriptStep(
script_name=”multiply.py”,
arguments=[“–x1”, x1_param, “–x2”, x2_param],
outputs=[multiply_output],
compute_target=cpu_cluster,
source_directory=project_folder
)
In this code snippet, PythonScriptStep is used for creating a step in the pipeline.
Step 3: Run and Publish the Pipeline
Once the custom component is created, it can be executed in a pipeline. After the pipeline run is completed successfully, you can publish it.
pipeline = Pipeline(workspace=ws, steps=[multiply_step])
pipeline_run = Experiment(ws, ‘Multiply_Numbers’).submit(pipeline)
published_pipeline = pipeline_run.publish_pipeline(
name=”MultiplyNumbers”, description=”Multiplies two numbers”, version=”1.0″)
This code creates a pipeline experiment named “Multiply_Numbers”, submits the pipeline to the Azure Machine Learning workspace, and then publishes the pipeline.
Step 4: Invoke the Pipeline
Once published, a REST endpoint is created for the pipeline. This REST endpoint can be invoked to run the pipeline.
response = requests.post(published_pipeline.endpoint, headers=aad_token, json={“ExperimentName”: “My_New_Experiment”})
This is a basic guide for creating custom components in Azure data science solutions. By encapsulating your code into custom components, you can simplify your pipeline setup, increase code reuse, and create more reliable workflows. Practice making your own to get a hang of it, the possibilities are endless!
Practice Test
True/False: Custom components in Azure can be created using Python, R, or SQL.
- Answer: True
Explanation: Azure ML allows you to create custom components using Python, R, or SQL, allowing flexibility and customization.
Multiple Select: What are some of the things you can do with custom modules in Azure?
- A. Create reusable pieces of logic or code
- B. Combine several Azure services into a single module
- C. Add additional services to Azure
- D. Customize and extend the functionality of pre-existing services
Answer: A, B, D
Explanation: Custom modules in Azure ML can encapsulate reusable pieces of logic, combine several Azure services in a single module, and extend or customize the functionality of pre-existing services. Adding additional services to Azure is not done through custom modules.
True/False: Custom components in Azure cannot be shared with other Data Scientists.
- Answer: False
Explanation: Custom components in Azure can be shared by packaging them into modules and publishing them into a workspace.
Single Select: Which language is not supported by Azure for custom modules?
- A. SQL
- B. Python
- C. C++
- D. R
Answer: C
Explanation: Azure does not support C++ for custom modules. However, it does support Python, R, and SQL.
True/False: Custom components in Azure can be published for other users.
- Answer: True
Explanation: You can package your custom components, publish them to a workspace, and share them with other users.
Multiple Select: What are the benefits of creating custom components in Azure?
- A. Increased flexibility
- B. Code reusability
- C. Increased Azure platform limitations
- D. Extending Azure functionalities
Answer: A, B, D
Explanation: Creating custom components in Azure provides increased flexibility, code reusability, and the ability to extend Azure functionalities. It does not increase Azure platform limitations.
True/False: Pre-built Azure components are always preferred over custom components.
- Answer: False
Explanation: While pre-built Azure components can provide convenience, custom components allow for more customization and flexibility, based on the specific needs of a project.
Single Select: What is the primary purpose of creating custom components in Azure?
- A. To reduce cost
- B. To extend or customize functionality
- C. To create new Azure services
- D. To replace existing Azure services
Answer: B
Explanation: Custom components are primarily used to extend or customize the functionality of existing Azure services.
True/False: Custom components in Azure are only written in Python.
- Answer: False
Explanation: Custom components in Azure can be written in Python, R, or SQL.
Multiple Select: Which of the following can be done in Azure using custom components?
- A. Custom data cleaning
- B. Custom data modeling
- C. Custom data storage
- D. Custom visualization
Answer: A, B, D
Explanation: Custom components in Azure can be used for a variety of data processing tasks like data cleaning, data modeling, and data visualizations. However, it cannot be used for custom data storage.
Interview Questions
What is a custom component in Azure?
A custom component in Azure refers to the custom modules or functions which can be added to Azure Machine Learning pipelines to execute specific tasks beyond the built-in components offered by Azure.
What languages can be used to create custom components in Azure?
Python and R can be used to create custom components in Azure.
What are the steps to create a custom component in Azure?
Steps to create a custom component involves coding the component in Python or R, creating an object, testing the component, and registering the component with Azure.
How do you test a custom component in Azure?
A custom component is tested by executing it in a local or cloud environment. The output of the component is used to verify the component works as expected.
How is a custom component registered in Azure?
A custom component is registered by saving it in a .zip or .whl (Python Wheel) file and uploading it to the Azure portal. It can also be registered through Azure Machine Learning SDK.
What is dockerization in the context of custom components in Azure?
Dockerization refers to the process of creating a Docker image that includes all the necessary dependencies for a custom component to run.
How does Azure automatically manage dependencies for a custom component?
Azure manages dependencies for custom components through a docker image which contains the runtime language (Python or R), all necessary libraries, and the code of the custom component.
What happens when a custom component is executed in an Azure pipeline?
When a custom component is executed in an Azure pipeline, a Docker container is spun up with the environment specified in the Docker image, and the custom component code is executed within this container.
How can you control the compute resources used by a custom component?
The compute resources used by a custom component can be controlled by specifying the compute target to use when running the pipeline.
What are the benefits of using custom components in Azure?
The benefits include reusability of code, encapsulation of complex tasks, and enhancement of the built-in machine learning capabilities of Azure.
Can you share a custom component with other users in Azure?
Yes, once a custom component is registered, it can be shared with other users.
Can a custom component be used in multiple pipelines?
Yes, once a custom component is registered, it can be used in multiple pipelines.
How can you debug a custom component in Azure?
Debugging a custom component usually involves checking the logs of the pipeline run, or running the component code locally to troubleshoot issues.
How can you version control custom components in Azure?
Azure Machine Learning provides a versioning system to manage custom components. Each registered component is associated with a unique version.
What are the prerequisites for creating a custom component in Azure Machine Learning?
To create custom components, you should have a base knowledge in Python or R programming and must be familiar with the Azure Machine Learning SDK.
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