Getting Started with Azure AI ML

Welcome to the Azure AI Machine Learning (Azure AI ML) community! This guide will help you get started with building, training, and deploying machine learning models on Azure.

What is Azure AI ML?

Azure AI ML is a cloud-based service that enables you to build, train, deploy, and manage machine learning models at scale. It provides a collaborative environment with end-to-end ML lifecycle management capabilities, empowering data scientists and developers to accelerate their ML projects.

Key Components

Getting Started Steps

1. Create an Azure AI ML Workspace

This is your central hub for all ML activities. You can create one through the Azure portal or using the Azure CLI.

                    az ml workspace create --name my-workspace --resource-group my-rg --location eastus
                

2. Set Up Your Development Environment

Choose how you want to develop your ML solutions.

Azure Machine Learning Studio: A web-based IDE for managing projects and running experiments.
VS Code Extension: Integrate Azure AI ML capabilities directly into your VS Code.
SDKs (Python, R): Programmatically interact with Azure AI ML services.

We recommend starting with the Azure ML Studio for a visual experience.

3. Prepare Your Data

Azure AI ML supports various data sources. You can upload data directly or connect to existing Azure storage.

Learn how to create a Dataset object to represent your data.

4. Train Your First Model

Use compute resources to train your model. Azure AI ML simplifies this process by managing compute provisioning and job submission.

Explore the Experiment and Run objects for tracking.

5. Deploy Your Model

Make your trained model accessible for predictions.

Learn about creating InferenceConfig and DeploymentConfig.