ML Ops in Azure

Setting up MLOps (Machine Learning Operations) in Azure involves creating a continuous integration and continuous deployment (CI/CD) pipeline to manage machine learning models efficiently. Below, I’ll provide a step-by-step guide to creating an MLOps pipeline in Azure using Azure Machine Learning Services, Azure DevOps, and Azure Kubernetes Service (AKS) as an example. This example assumes you already have an Azure subscription and some knowledge of Azure services. You can check out for FREE learning resources at https://learn.microsoft.com/en-us/training/azure/

Step 1: Prepare Your Environment

Before you start, make sure you have the following:

- An Azure subscription.

- An Azure DevOps organization.

- Azure Machine Learning Workspace set up.

Step 2: Create an Azure DevOps Project

1. Go to Azure DevOps (https://dev.azure.com/) and sign in.

2. Create a new project that will host your MLOps pipeline.

Step 3: Set Up Your Azure DevOps Repository

1. In your Azure DevOps project, create a Git repository to store your machine learning project code.

Step 4: Create an Azure Machine Learning Experiment