Organizing an Azure ML project with multiple step scripts and shared modules: A Step-by-Step Guide

As machine learning projects grow in complexity, it’s essential to keep your project organized and efficient. Azure Machine Learning (Azure ML) provides a robust platform for building, training, and deploying machine learning models. In this article, we’ll dive into the best practices for organizing an Azure ML project with multiple step scripts and shared modules.

Why Organize Your Azure ML Project?

Before we dive into the nitty-gritty of organizing an Azure ML project, let’s explore the importance of keeping your project structured:

• Easier collaboration: A well-organized project makes it easier for team members to understand the project structure and contribute to it.
• Faster development: With a clear project structure, you can quickly identify and reuse existing code, reducing development time and effort.
• Better maintenance: An organized project is easier to maintain and update, reducing the risk of errors and bugs.
• Improved scalability: A well-structured project can handle complex models and large datasets, making it easier to scale your project as needed.

Step 1: Create a Clear Project Structure

A clear project structure is essential for organizing an Azure ML project. Follow these steps to create a well-structured project:

1. Create a new Azure ML workspace or navigate to an existing one.
2. Create a new folder for your project by clicking on the “New folder” button in the Azure ML workspace.
3. Navigate to the new folder and create the following subfolders:
   • data: Store your datasets and data sources here.
   • models: Store your trained models and model definitions here.
   • scripts: Store your scripts and code files here.
   • shared: Store shared modules and utilities here.
   • outputs: Store your project outputs, such as model predictions and evaluation metrics.

project_folder/
data/
models/
scripts/
shared/
outputs/

Step 2: Create Shared Modules

Shared modules are reusable code pieces that can be used across your project. They help reduce code duplication and make it easier to maintain your project. Follow these steps to create shared modules:

1. Navigate to the shared folder and create a new Python file, e.g., utils.py.
2. Define a Python module with reusable functions or classes, e.g.:

       # utils.py
       def load_data(dataset_name):
           # Load dataset from Azure ML dataset
           dataset = ...
           return dataset
   
       def preprocess_data(dataset):
           # Preprocess dataset
           preprocessed_data = ...
           return preprocessed_data
       

3. Save the utils.py file.

Step 3: Create Multiple Step Scripts

Multiple step scripts are a key feature in Azure ML that allow you to break down complex workflows into manageable steps. Follow these steps to create multiple step scripts:

1. Navigate to the scripts folder and create a new Python file, e.g., step1_data_load.py.
2. Define a Python script that loads a dataset using the shared utils.py module, e.g.:

       # step1_data_load.py
       from shared.utils import load_data
   
       dataset_name = "my_dataset"
       dataset = load_data(dataset_name)
       print("Loaded dataset:", dataset_name)
       

3. Create additional scripts for each step in your workflow, e.g. step2_data_preprocess.py, step3_model_train.py, etc.
4. Save each script file.

Step 4: Create an Azure ML Pipeline

Azure ML pipelines allow you to orchestrate multiple step scripts into a single workflow. Follow these steps to create an Azure ML pipeline:

1. Navigate to the Azure ML workspace and click on “Pipelines” in the left-hand menu.
2. Click on the “New pipeline” button.
3. Drag and drop the script files from the scripts folder into the pipeline designer.
4. Configure each script step by selecting the correct Python interpreter and environment.
5. Connect the script steps in the correct order, e.g. step1_data_load.py -> step2_data_preprocess.py -> step3_model_train.py.
6. Save the pipeline.

Script Step	Description
step1_data_load.py	Load dataset
step2_data_preprocess.py	Preprocess dataset
step3_model_train.py	Train machine learning model

Step 5: Run and Monitor Your Pipeline

Once you’ve created your pipeline, it’s time to run and monitor it:

1. Click on the “Submit” button to run the pipeline.
2. Monitor the pipeline run by clicking on the “Runs” tab in the Azure ML workspace.
3. View the output of each script step and troubleshoot any errors.

Conclusion

Organizing an Azure ML project with multiple step scripts and shared modules is crucial for building efficient and scalable machine learning workflows. By following the steps outlined in this article, you can create a well-structured project that’s easy to maintain and scale. Remember to:

• Create a clear project structure with separate folders for data, models, scripts, and shared modules.
• Create reusable shared modules to reduce code duplication.
• Break down complex workflows into multiple step scripts.
• Create an Azure ML pipeline to orchestrate the script steps.
• Run and monitor your pipeline to troubleshoot any errors.

By following these best practices, you’ll be able to build efficient and scalable machine learning projects with Azure ML.

Additional Resources

For more information on Azure ML and machine learning, check out the following resources:

• Azure Machine Learning documentation: https://docs.microsoft.com/en-us/azure/machine-learning/
• Azure ML tutorials: https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-1st-experiment-brushed-steel
• Machine learning courses on Microsoft Learn: https://docs.microsoft.com/en-us/learn/browse/?products=azure%2Fmachine-learning

Frequently Asked Question

Get ready to organize your Azure Machine Learning (Azure ML) project like a pro! Here are the top 5 FAQs to help you master the art of managing multiple step scripts and shared modules.