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Azure Machine Learning Studio (Azure ML) is a powerful platform for building, training, and deploying machine learning models. This guide will walk you through creating a new workspace, registering a model, setting up a custom environment, and deploying a model to an endpoint
Step 1: Create a New Workspace
What is an Azure ML Workspace?
A workspace is a foundational resource in Azure ML that provides a centralized place to manage machine learning experiments, resources, and assets.
Steps to Create a Workspace
- Log in to Azure Portal: Go to ml.azure.com.
- Create a New Workspace:
- Click on + Create.
- Fill in the required details:
- Subscription: Select your Azure subscription.
- Resource Group: Choose an existing one or create a new one.
- Workspace Name: Provide a unique name for your workspace.
- Region: Select the region closest to your team or resources.
- Click Review + Create and then Create.
Step 2: Register Your Model
Why Register a Model?
Model registration ensures version control and enables easy deployment and collaboration within your team.
Steps to Register a Model
- Log in to Azure ML Studio:
- Go to your workspace in Azure ML Studio ().
- Register the Model:
- Navigate to Assets > Models > Register Model.
- Upload your model file (e.g., .pkl, .onnx, or .mlmodel).
- Provide details such as:
- Model Name: Give it a unique name.
- Description: Briefly describe the model.
- Optionally, tag your model for better organization.
- Complete Registration:
- Click Register to store the model in the workspace
Step 3: Create a Custom Environment
What is an Environment in Azure ML?
An environment encapsulates the dependencies required for model training or inference, such as Python packages, system libraries, and environment variables.
Steps to Create a Custom Environment
- Navigate to Environments:
- In Azure ML Studio, go to Assets > Environments.
Create a New Environment:
- Click + New Environment.
- Choose Custom Environment and provide:
- Name: A unique name for the environment.
- Description: Details about the environment’s purpose.
Specify Dependencies:
- Using a YAML File: Upload a .yml file containing your dependencies.
- Manually Add Dependencies:
- Choose a base image (e.g., AzureML TensorFlow or AzureML PyTorch).
- Add specific Python packages in the Conda or Pip section.
- Save the Environment
- Review the configuration and click Create.
Step 4: Deploy a Model to an Endpoint
What is an Endpoint?
Endpoints expose your model as a web service, allowing applications to interact with it via REST APIs.
Steps to Deploy a Model
- Prepare Your Model and Environment:
- Ensure the model and environment are registered in the workspace.
- Create a Deployment:
- Go to Endpoints > Real-time Endpoints > + New Endpoint.
- Provide details:
- Name: A unique name for the endpoint.
- Compute Type: Choose between managed online endpoints or Kubernetes.
- Specify Deployment Configuration:
- Model: Select the registered model.
- Environment: Choose the custom environment you created.
- Inference Configuration: Define the entry script (e.g., score.py) and other runtime settings.
- Click Deploy and monitor the deployment status.
- Test the Endpoint:
- Once deployed, use the endpoint URL and API key to send test requests using tools like Postman or Python’s requests library.
Conclusion
Azure Machine Learning Studio streamlines the entire machine learning lifecycle, from model development to deployment. By following the steps outlined above, you can effectively manage resources, ensure reproducibility, and deploy your models with ease.