Develop AI cloud solutions on Azure (AI-200)

Duration: 5 days

This hands-on course teaches developers how to create, integrate, secure, and operate AI-driven cloud applications on Azure using modern compute, data, and messaging services.

Who Should Attend

• Backend developers building AI-enabled applications on Azure
• Cloud developers implementing containerized and serverless workloads
• Engineers integrating event-driven and message-based application services
• Teams preparing for the Azure AI Cloud Developer Associate certification

Prerequisites

• Experience building backend services with C#, JavaScript, Python, or Java
• Basic Azure knowledge (resource groups, networking, identity, and RBAC)
• Familiarity with REST APIs, JSON, and asynchronous communication patterns
• Basic understanding of containers, Git, and CI/CD workflows

Learning Goals

• Build and host AI applications with Azure Container Apps and Azure Kubernetes Service
• Develop serverless APIs and background processing with Azure Functions
• Integrate distributed services using Azure Service Bus and Event Grid
• Design AI-ready data architectures with Cosmos DB, PostgreSQL, and Redis
• Apply secure configuration, secret management, and identity-based access controls
• Implement monitoring, troubleshooting, and reliability practices for production AI apps

Course Content

1. Host and run AI applications on Azure
   • Containerized hosting with Azure Container Apps
   • Deployment and scaling strategies on Azure Kubernetes Service
   • Operational patterns for resilient cloud workloads
2. Build serverless and integration components
   • API and workflow development with Azure Functions
   • Event-driven design with Event Grid
   • Reliable messaging and orchestration with Service Bus
3. Design data services for AI workloads
   • Data modeling and querying with Azure Cosmos DB for NoSQL
   • Vector-enabled scenarios with Azure Database for PostgreSQL
   • Caching and streaming patterns using Azure Managed Redis
4. Connect services and secure the platform
   • Backend integration and service-to-service communication
   • Managing application secrets and configuration
   • Identity, authorization, and secure deployment practices
5. Observe and troubleshoot AI cloud applications
   • Logging, metrics, and distributed tracing patterns
   • Diagnosing performance and reliability issues
   • Operating AI solutions with continuous improvement loops

Hands-on Labs

• Deploy a containerized API to Azure Container Apps and validate scaling behavior
• Host a microservice on AKS and configure production-ready health checks
• Build an Azure Functions workflow that reacts to Event Grid events
• Integrate Service Bus queues and topics into a distributed backend solution
• Implement data storage patterns across Cosmos DB, PostgreSQL, and Redis
• Configure managed identity, secrets, and application configuration securely
• Add observability dashboards and troubleshoot a failing AI workflow

Outcomes

After this course, participants can build secure and scalable AI cloud solutions on Azure, connect distributed services and data platforms, and operate production workloads with confidence.

Development Environment

• Windows, macOS, or Linux
• Visual Studio 2022/2026 or Visual Studio Code
• .NET 10 SDK (or equivalent runtime/language toolchain for selected labs)
• Azure CLI, Docker, and kubectl
• Access to an Azure subscription with permissions to create resources