Azure AI Machine Learning Deployment

Resource Group: Houses your infrastructure for the deployment.
AI Model: The core machine learning model to be deployed.
Deployment Pipeline: Automates the build, testing, and deployment steps.
Configuration: Settings for your model, data, and infrastructure.

Introduction

Welcome to the official documentation for Azure AI Machine Learning Deployment.

This guide provides a comprehensive overview of the process, from initial setup to deployment, ensuring a smooth and efficient experience.

1. Sign in to your Azure account. Ensure you have the necessary permissions to create and deploy resources.

2. Navigate to the Azure AI Machine Learning service in the portal.

3. Select "Deployment" from the left-hand navigation menu.

4. Start the deployment process by clicking the "Deploy" button.

The deployment process involves several key components:

Azure Resource Manager (ARM) Template: Create an ARM template to define the infrastructure.
Deployment Script: Use a deployment script (e.g., Azure CLI) to deploy the template.
Testing: Run automated tests to verify the model's functionality.
Monitoring: Track the model's performance and identify potential issues.