This course teaches developers how to create, monitor, and troubleshoot AI solutions on Microsoft Azure. Students will learn how to implement Azure compute and containerization patterns to host applications, build serverless APIs with Azure Functions, and integrate services using event-driven and message-based architectures such as Azure Service Bus and Event Grid. The course also covers working with Azure data services that support AI workloads, including designing and querying solutions with Cosmos DB for NoSQL, Azure Database for PostgreSQL with pgvector, and Azure Managed Redis for caching, streaming, and vector search. By the end of the course, developers will be able to connect services, orchestrate AI workflows, and build secure, scalable, and observable AI-driven applications on Azure.
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Course Outline
1. Store and manage containers in Azure Container Registry
• Registries, repositories, and artifacts
• Build and run images with ACR Tasks
• Tag and version images
2. Deploy containers to Azure App Service
• Deploy containers to Azure App Service
• Configure container runtime behavior
• Configure application settings
• Observe and troubleshoot containerized apps
3. Run sidecar-enabled AI applications on Azure App Service
• Choose the App Service sidecar pattern
• Configure main and sidecar containers
• Connect containers and share files
• Diagnose sidecar failures
4. Deploy containers to Azure Container Apps
• Explore Container Apps environments
• Deploy a container app using the Azure CLI and YAML
• Configure runtime settings with environment variables and secrets
• Configure image pull authentication for private registries
• Verify deployments with logs and status
5. Manage containers in Azure Container Apps
• Update images and manage revisions safely
• Manage the container app lifecycle
• Monitor logs and troubleshoot issues
• Configure health probes and troubleshoot failures
• Optimize container resources and scaling
6. Scale containers in Azure Container Apps
• Configure scale rules
• Implement event-driven scaling with KEDA
• Apply KEDA scalers for custom workloads
• Select compute resources for performance and cost
• Choose and apply revision modes
7. Run AI-generated code securely in Azure Container Apps dynamic sessions
• Choose a dynamic session pool
• Configure a secure session pool
• Execute code and exchange files
• Manage lifecycle and execution failures
8. Deploy applications to Azure Kubernetes Service
• Create Kubernetes deployment manifests
• Expose applications in Azure Kubernetes Services
• Deploy applications to Azure Kubernetes Services
9. Configure applications on Azure Kubernetes Service
• Define ConfigMaps for application settings
• Implement secrets for sensitive data
• Attach persistent storage to an app
10. Monitor and troubleshoot applications on Azure Kubernetes Service
• Monitor application logs and metrics
• Troubleshoot pods and services
• Verify service connectivity and endpoints
11. Build queries for Azure Cosmos DB for NoSQL
• Explore Azure Cosmos DB for NoSQL
• Implement the Azure Cosmos DB for NoSQL SDK
• Query Azure Cosmos DB for NoSQL
12. Implement vector search on Azure Cosmos DB for NoSQL
• Store and retrieve embeddings in Azure Cosmos DB
• Execute vector similarity queries for semantic search
• Combine vector similarity results with metadata filtering
• Use the change feed to trigger embedding refresh
13. Optimize query performance for Azure Cosmos DB for NoSQL
• Understand indexes in Azure Cosmos DB
• Configure range and composite indexes
• Tune vector indexes for embedding workloads
• Reduce RU costs with strategic indexing
• Choose consistency levels for optimal performance
14. Build and query with Azure Database for PostgreSQL
• Explore Azure Database for PostgreSQL
• Connect to PostgreSQL
• Create and manage schemas
• Query data
• Integrate SDKs and applications
15. Implement vector search with Azure Database for PostgreSQL
• Store and query embeddings with pgvector
• Perform fast vector similarity search
• Manage index lifecycle and embedding updates
• Run vector similarity search for semantic retrieval
• Implement retrieval patterns for RAG pipelines
16. Optimize vector search in Azure Database for PostgreSQL
• Tune PostgreSQL for pgvector
• Choose and configure vector indexes
• Optimize data layout
• Scale for high-volume workloads
• Connection optimization
17. Implement data operations in Azure Managed Redis
• Explore Azure Managed Redis
• Client libraries and development best practices
• Implement data operations
18. Implement event messaging with Azure Managed Redis
• Publish and subscribe to events with Redis pub/sub
• Implement task queues with Redis Streams
• Choose between broadcast and coordinated distribution
19. Implement vector storage in Azure Managed Redis
• Index and query vector data
• Choose vector types and indexing strategies
• Optimize Redis data structures for vector storage
20. Queue and process AI operations with Azure Service Bus
• Explore Azure Service Bus concepts and messaging in AI architectures
• Choose between queues and topics with subscriptions
• Structure messages for AI workloads
• Process messages reliably
21. Develop event-driven AI workflows with Azure Event Grid
• Understand Azure Event Grid concepts and event-driven patterns for AI solutions
• Work with event schemas and properties
• Configure delivery and retry policies for reliable event processing
• Publish custom events from AI applications
22. Build serverless AI backends with Azure Functions
• Understand Azure Functions hosting and scaling for AI workloads
• Set up the local development environment for Functions
• Create triggers and bindings for AI integration patterns
• Manage secrets and configuration in Functions
• Configure identity and access for Functions
23. Orchestrate durable AI workflows with Azure Durable Functions
• Separate orchestration logic from activity work
• Process independent work with fan-out/fan-in
• Coordinate human approval with external events
• Design reliable workflow steps
24. Manage application secrets with Azure Key Vault
• Store and organize secrets, keys, and certificates
• Retrieve secrets using Azure SDK client libraries
• Handle secret versioning and rotation
• Implement caching strategies to reduce Key Vault calls
25. Manage application settings with Azure App Configuration
• Connect to App Configuration from application code
• Organize settings with labels and feature flags
• Reference Key Vault secrets from App Configuration
• Decide what to store in App Configuration vs Key Vault
26. Instrument an app with OpenTelemetry
• Explore OpenTelemetry and its role in observability
• Add the OpenTelemetry SDK to an application
• Configure spans and traces
• Export telemetry to Azure Monitor
• Debug distributed flows with trace data
27. Analyze app telemetry with logs and metrics
• Write basic KQL queries
• Explore logs for errors and performance
• Build dashboards for app telemetry
• Create workbooks for interactive analysis
• Set alerts for app failures and anomalies
Course Details
• Programming experience with Python (some learning paths also accept JavaScript or C#).
• Basic understanding of Azure services and cloud computing concepts.
• Familiarity with containerization fundamentals, including Kubernetes fundamentals and command-line tools such as Azure CLI.
• Familiarity with relational databases and SQL fundamentals, and with JSON document structures.
• Understanding of machine learning concepts including embeddings and similarity search.
• Knowledge of caching concepts and data structures, and basic understanding of message queuing patterns.
• Familiarity with distributed systems concepts, event-driven architectures, REST APIs, and asynchronous messaging patterns.
• Familiarity with distributed application architectures.
• Access to Azure Kubernetes Service and Azure Managed Redis and related tools for hands-on practice.
This course is designed for developers who build backend and AI-driven applications on Azure and need practical skills in containerized compute, data services for AI, event-driven workflows, and application security and monitoring.
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