Azure AI ML Deployment

This section provides comprehensive guidance and best practices for deploying your machine learning models using Azure Machine Learning (Azure AI ML). Effective deployment is crucial for making your trained models available for inference and integrating them into your applications and workflows.

Introduction to Azure AI ML Deployment

Azure AI ML offers a variety of options for deploying your models, catering to different scenarios such as real-time inference, batch scoring, and edge deployments. Understanding these options is key to choosing the right deployment strategy for your needs.

Deployment Targets

• Azure Kubernetes Service (AKS): A managed Kubernetes service for deploying and scaling containerized ML models. Ideal for high availability and scalability.
• Managed Endpoints: A fully managed service that simplifies the deployment and serving of models without the need to manage underlying infrastructure. Supports both online and batch endpoints.
• Azure Container Instances (ACI): A simple way to deploy a containerized model to Azure without managing virtual machines or orchestration. Good for development, testing, or low-scale scenarios.
• Azure Functions: Serverless compute that allows you to run your code on-demand, suitable for event-driven inference.
• Edge Devices: Deploy models to IoT Edge devices for offline or low-latency inference.

Online Endpoints

Online endpoints provide real-time scoring for your models. They are designed for low-latency, high-throughput inference.

Key Features:

• Scalability and high availability.
• REST API endpoints for easy integration.
• Support for authentication and authorization.
• Monitoring and logging capabilities.

Deployment Steps:

1. Register your model: Ensure your trained model is registered in the Azure AI ML workspace.
2. Create an inference script: Write a Python script that loads your model and defines the logic for scoring new data.
3. Define an environment: Specify the dependencies required for your inference script and model.
4. Create an online endpoint: Configure the endpoint with desired compute resources and authentication settings.
5. Deploy the model to the endpoint: Package your model, inference script, and environment, and deploy it to the created endpoint.

Batch Endpoints

Batch endpoints are used for scoring large volumes of data asynchronously. This is ideal for scenarios where real-time responses are not required.

Key Features:

• Processing large datasets efficiently.
• Asynchronous execution.
• Integration with Azure data services.

Deployment Steps:

1. Register your model and create an inference script similar to online deployments.
2. Create a batch endpoint.
3. Submit a batch inference job, specifying input data locations and output locations.

Best Practices

• Version Control: Manage different versions of your models and deployment configurations.
• Monitoring: Implement robust monitoring for latency, error rates, and resource utilization.
• Security: Secure your endpoints with appropriate authentication and authorization mechanisms.
• Containerization: Use Docker containers to ensure consistent deployment environments.
• CI/CD: Integrate your deployment process into a Continuous Integration/Continuous Deployment pipeline for automation.

Resources

• Deploy models with online endpoints
• Deploy models with batch endpoints
• Compute targets for training and deployment