Deploy Your First Azure AI Service in a Container

Azure AI Services containers enable you to deploy Microsoft's pre-built AI capabilities in containerized environments, providing flexibility for on-premises deployment, edge computing scenarios, and situations requiring specific data residency. Unlike traditional cloud-only AI services, containers allow you to run AI workloads locally. This hybrid approach is particularly valuable for organizations that need to process sensitive data on-premises, work in disconnected environments, or require low-latency AI processing at the edge.

In this lab, you will deploy an Azure AI Services sentiment analysis container to Azure Container Instances and test its functionality using REST API calls. You'll learn how to provision the required Azure resources, configure container deployments with proper authentication, and verify that your containerized AI service processes requests identically to its cloud-based counterpart.

Objectives

Upon completion of this intermediate level lab, you will be able to:

• Create an Azure AI Services multi-service resource for container authentication and billing
• Deploy Azure AI Services containers to Azure Container Instances with proper configuration
• Configure environment variables for container-to-cloud connectivity and billing integration
• Test containerized AI services using REST API endpoints to verify functionality
• Analyze sentiment analysis results including document-level and sentence-level classifications

Who is this lab for?

This lab is designed for:

• Cloud Developers building AI-enabled applications that require flexible deployment options
• DevOps Engineers implementing containerized AI solutions in hybrid cloud environments
• Solutions Architects designing AI architectures with on-premises or edge computing requirements
• AI-102 Certification Candidates preparing for the Azure AI Engineer Associate certification exam
• Data Scientists exploring deployment strategies for AI models in production environments