- Duration: 5 days
Overview
This course is designed for developers who want to build, deploy, monitor, and troubleshoot AI-powered cloud solutions on Microsoft Azure.
Participants will learn how to implement Azure compute and containerization patterns for hosting AI applications, develop serverless APIs using Azure Functions, and integrate cloud services through event-driven and message-based architectures using Azure Service Bus and Azure Event Grid.
The course also covers Azure data services that support modern AI workloads, including Azure Cosmos DB for NoSQL, Azure Database for PostgreSQL with pgvector, and Azure Managed Redis. Participants will learn how to implement vector search, caching, messaging, event-driven processing, and secure application configurations.
By the end of the course, participants will be able to connect Azure services, orchestrate AI workflows, and develop secure, scalable, highly available, and observable AI-driven applications.
What You’ll Learn
- Understand the core principles of developing AI cloud solutions on Microsoft Azure
- Implement containerized application hosting using Azure services
- Deploy and manage applications using Azure Container Apps
- Deploy, manage, and monitor applications on Azure Kubernetes Service (AKS)
- Develop AI solutions using Azure Cosmos DB for NoSQL and vector search
- Implement AI-powered data solutions using Azure Database for PostgreSQL and pgvector
- Use Azure Managed Redis for caching, messaging, and vector-based workloads
- Build event-driven and message-based AI architectures using Azure Service Bus and Event Grid
- Develop serverless AI backends using Azure Functions and Durable Functions
- Secure application secrets and configurations using Azure Key Vault and Azure App Configuration
- Implement observability using Azure Monitor, Application Insights, OpenTelemetry, and KQL
- Monitor, troubleshoot, and optimize distributed AI applications
- Build secure, scalable, reliable, and production-ready AI cloud solutions
Who Should Attend
This course is designed for:
- Software Developers
- Backend Developers
- AI Developers
- Cloud Developers
- Application Developers
- AI Engineers
- Cloud Engineers
- Solutions Developers
- Software Engineers
It is particularly suitable for developers who build backend and AI-driven applications on Azure and require practical experience with containerized compute, AI-focused data services, event-driven architectures, application security, and cloud monitoring.
Prerequisites
Before attending this course, participants should have:
- Programming experience with languages such as Python, JavaScript, or C#
- Basic understanding of Microsoft Azure and cloud computing concepts
- Familiarity with Azure services and the Azure portal
- Basic understanding of containerization concepts
- Familiarity with application development and backend services is recommended
Curriculum
Module 01: Implement Container Application Hosting on Azure
Learn the core container hosting capabilities available on Azure for AI applications and backend services.
Topics include:
- Azure Container Registry
- Container image management
- Building container images using ACR Tasks
- Image tagging and versioning
- Deploying custom containers to Azure App Service
- Configuring ports and startup commands
- Persistent storage for container applications
- Managing environment-specific application settings
Module 02: Deploy and Manage Apps on Azure Container Apps
Learn how to deploy and manage containerized applications using Azure Container Apps.
Topics include:
- Creating and deploying container apps
- Configuring Container Apps environments
- Environment variables and secrets
- Container registry authentication
- Updating container images
- Managing application revisions
- Monitoring application logs
- Configuring health probes
- Automatic horizontal scaling
- HTTP-based scaling rules
- KEDA scalers
- Traffic management
- Azure Container Apps dynamic sessions
- Secure execution of AI-generated and user-submitted code
- File exchange and session lifecycle management
Module 03: Deploy and Monitor Applications on Azure Kubernetes Service
Learn how to deploy, configure, monitor, and troubleshoot applications running on Azure Kubernetes Service (AKS).
Topics include:
- Kubernetes deployment manifests
- Kubernetes Services
- Application configuration using ConfigMaps
- Secure configuration using Secrets
- Persistent storage
- Application logs and metrics
- Monitoring application health
- Troubleshooting pods and Services
- Connectivity and application access verification
Module 04: Develop AI Solutions with Azure Cosmos DB for NoSQL
Learn how Azure Cosmos DB for NoSQL can provide a scalable data foundation for AI applications.
Topics include:
- Azure Cosmos DB resource model
- SDK integration
- CRUD operations
- SQL queries
- Document data retrieval
- Vector search
- Storing AI embeddings
- Similarity search using VectorDistance
- Metadata filtering
- Hybrid search
- Change feed integration
- Synchronizing embeddings
- Query performance optimization
- Range and composite indexes
- Vector index selection
- Consistency levels
- Balancing performance, freshness, and cost
Module 05: Develop AI Solutions with Azure Database for PostgreSQL
Learn how to build AI-enabled applications using Azure Database for PostgreSQL and the pgvector extension.
Topics include:
- PostgreSQL schema design
- Efficient SQL queries
- Secure Python integration
- Microsoft Entra authentication
- pgvector extension
- Storing AI embeddings
- Vector similarity search
- Distance metrics
- Retrieval-Augmented Generation (RAG)
- Semantic search
- Recommendation scenarios
- Vector index selection
- PostgreSQL and pgvector optimization
- Efficient data layout
- Scaling for high-volume AI workloads
- Connection pooling
Module 06: Enhance AI Solutions with Azure Managed Redis
Learn how Azure Managed Redis can improve the performance and capabilities of AI applications.
Topics include:
- Caching strategies for AI applications
- Redis data operations
- Event messaging
- Data storage and retrieval
- Vector storage
- Improving application performance
- Supporting low-latency AI workloads
Module 07: Integrate Backend Services for AI Solutions
Learn how to build reliable backend architectures that support AI applications using Azure messaging, event-driven, and serverless services.
Topics include:
- Azure Service Bus
- Queues and topics
- Decoupling AI application components
- Processing inference requests
- Dead-letter queues
- Reliable message processing
- Azure Event Grid
- Event-driven application architectures
- Event routing
- Delivery policies
- Publishing custom events
- Azure Functions
- Serverless AI backends
- Durable Functions
- Long-running workflows
- Parallel processing
- Human approval workflows
- Failure recovery and orchestration
Module 08: Manage Application Secrets and Configuration for AI Solutions
Learn how to securely manage application secrets and centralized configuration for AI cloud solutions.
Topics include:
- Azure Key Vault
- Secure secret storage
- Managed identity authentication
- Secret versioning
- Secret rotation
- Zero-downtime credential updates
- Credential caching strategies
- Azure App Configuration
- Centralized application settings
- Key-value configuration
- Environment-specific configuration using labels
- Feature flags
- Controlled application rollouts
- Integrating Key Vault secrets with application configuration
Module 09: Observe and Troubleshoot Apps on Azure
Learn how to implement end-to-end observability and troubleshoot distributed AI applications running on Azure.
Topics include:
- OpenTelemetry
- Distributed tracing
- Custom spans
- Azure Monitor
- Application Insights
- Telemetry collection and analysis
- Kusto Query Language (KQL)
- Error analysis
- Performance monitoring
- Performance trends
- Operational dashboards
- Azure Monitor Workbooks
- Application alerts
- Failure and anomaly detection
- Troubleshooting distributed applications
Course Outcome
Upon successful completion of this course, participants will be able to design, develop, deploy, secure, monitor, and troubleshoot AI-powered cloud applications on Microsoft Azure using containerized compute, serverless technologies, AI-ready data services, messaging and event-driven architectures, secure configuration management, and modern observability tools.