Understanding and Managing Azure ML Models

Azure Machine Learning provides a robust framework for managing the lifecycle of your machine learning models. This article delves into the concept of a model within Azure ML, how to register them, track their versions, and prepare them for deployment.

What is an Azure ML Model?

In Azure Machine Learning, a model is a serialized object that represents a trained machine learning algorithm. This object can be a file or a folder containing files, such as:

• A scikit-learn model saved using joblib or pickle.
• A TensorFlow, PyTorch, or ONNX model.
• A model trained using Azure ML's built-in algorithms or AutoML.

Once trained, your model needs to be registered with your Azure ML workspace to enable versioning, tracking, and deployment. This registration process creates an artifact that references the model's files and associated metadata.

Registering Models

You can register models using the Azure ML SDK (Python) or the Azure CLI. Registration typically involves specifying the local path to your model files and providing a name and description.

Using the Azure ML SDK (Python)

The following Python code snippet demonstrates how to register a model:

from azure.ai.ml import MLClient
from azure.ai.ml.entities import Model
from azure.identity import DefaultAzureCredential

# Authenticate and get ML client
credential = DefaultAzureCredential()
ml_client = MLClient(
    credential=credential,
    subscription_id="YOUR_SUBSCRIPTION_ID",
    resource_group_name="YOUR_RESOURCE_GROUP",
    workspace_name="YOUR_WORKSPACE_NAME",
)

# Define model path and name
model_path = "path/to/your/local/model/folder"
model_name = "my-scikit-learn-model"
model_version = "1" # Optional: specify version

# Create a Model object
azureml_model = Model(
    path=model_path,
    name=model_name,
    version=model_version,
    description="A scikit-learn model for customer churn prediction."
)

# Register the model
registered_model = ml_client.models.create_or_update(azureml_model)

print(f"Model registered: {registered_model.name} (Version: {registered_model.version})")
            

Using the Azure CLI

You can also register models using the Azure CLI:

az ml model create --name my-scikit-learn-model --version 1 --path ./model/ --description "Scikit-learn churn prediction model"
            

Model Versioning and Management

Azure ML automatically handles versioning of your registered models. When you register a model with the same name but a different version, Azure ML creates a new version of that model. This is crucial for tracking experiments, reproducibility, and managing different iterations of your AI solutions.

You can view all registered models and their versions in the Azure ML studio or programmatically via the SDK/CLI. This allows you to easily compare performance, revert to previous versions, or select the best-performing model for deployment.

Model Metadata

When registering a model, you can associate it with valuable metadata, such as:

• Description: A human-readable explanation of the model's purpose.
• Tags: Key-value pairs for categorizing and searching models (e.g., {"domain": "finance", "task": "classification"}).
• Custom Properties: Additional metadata specific to your workflow.

This metadata enhances discoverability and helps in organizing your model inventory.

Next Steps

Once your models are registered and versioned, you can proceed to the next critical step: deployment. Refer to the Model Deployment article for detailed instructions on how to serve your models as real-time endpoints or batch inference jobs.