Module 1: Implement container application hosting on Azure
This learning path guides you through core container hosting workflows on Azure for AI applications and backend services. You use Azure Container Registry to store and organize images, build images in the cloud with ACR Tasks, and apply tagging and versioning practices that support reliable deployments. From there, you deploy custom containers to Azure App Service, configure runtime behavior such as ports, startup commands, and persistent storage, and externalize environment-specific configuration using application settings.
Module 2: Deploy and manage apps on Azure Container Apps
This learning path guides you through the complete lifecycle of running containerized applications on Azure Container Apps. You start by deploying container apps to environments, configuring runtime settings with environment variables and secrets, and setting up registry authentication. You then manage the day-two lifecycle by updating images, managing revisions, monitoring logs, and configuring health probes. Next, you configure automatic horizontal scaling using HTTP rules, KEDA scalers, and traffic management to optimize performance and cost. Finally, you use Azure Container Apps dynamic sessions to securely execute AI-generated and user-submitted code in isolated environments, exchange files, and manage session lifecycle and failures.
Module 3: Deploy and monitor applications on Azure Kubernetes Service
This learning path guides you through the complete lifecycle of running applications on Azure Kubernetes Service. You start by creating deployment manifests and exposing applications with Kubernetes Services. You then externalize configuration using ConfigMaps, secure sensitive settings with Secrets, and attach persistent storage for stateful workloads. Finally, you learn to monitor application health using logs and metrics, troubleshoot pod and Service issues, and verify connectivity paths to ensure reliable access to your applications.
Module 4: Develop AI solutions with Azure Cosmos DB for NoSQL
This learning path guides you through developing AI solutions using Azure Cosmos DB for NoSQL. You start by building a data foundation with the Cosmos DB resource model, SDK integration, CRUD operations, and SQL queries to retrieve document data for AI applications.
You then implement vector search capabilities to store embeddings, execute similarity queries using the VectorDistance function, combine vector search with metadata filters and hybrid search, and use the change feed to keep embeddings synchronized.
Finally, you optimize query performance by analyzing query patterns, configuring range and composite indexes, selecting vector index types, and choosing consistency levels that balance freshness with cost efficiency.
Module 5: Develop AI solutions with Azure Database for PostgreSQL
This learning path guides you through developing AI solutions using Azure Database for PostgreSQL. You start by building a data foundation with schema design, efficient SQL queries, and secure Python integration using Microsoft Entra authentication.
You then implement vector search using the pgvector extension to store embeddings, execute similarity searches with different distance metrics, and build retrieval patterns that integrate with RAG pipelines for semantic search and recommendations.
Finally, you optimize vector search performance by tuning PostgreSQL and pgvector configuration, selecting appropriate vector indexes, designing efficient data layouts, scaling for high-volume workloads, and implementing connection pooling for AI applications.
Module 6: Enhance AI solutions with Azure Managed Redis
Learn how to use Azure Managed Redis to enhance your AI solutions, including caching strategies, data operations, event messaging, and vector storage.
Module 7: Integrate backend services for AI solutions
This learning path teaches you how to build and integrate backend services that support AI solutions on Azure. You start by using Azure Service Bus to decouple AI application components, queue inference requests, and process messages reliably with queues, topics, and dead-letter queues. You then build event-driven workflows with Azure Event Grid to route events between services with low latency, configure delivery policies, and publish custom events from AI applications. Finally, you create serverless AI backends with Azure Functions and use Durable Functions to coordinate long-running workflows with parallel processing, human approval, and reliable failure recovery.
Module 8: Manage application secrets and configuration for AI solutions
This learning path teaches you how to securely manage secrets and centralize configuration for AI solutions on Azure. You start by using Azure Key Vault to store, organize, and retrieve secrets with managed identity authentication, handle secret versioning and rotation for zero-downtime credential updates, and implement caching strategies that reduce API calls while maintaining credential freshness. You then use Azure App Configuration to centralize application settings, organize key-value pairs with labels for environment-specific variants, manage feature flags for controlled rollouts, and reference Key Vault secrets so the application retrieves configuration and secrets through a single path.
Module 9: Observe and troubleshoot apps on Azure
This learning path teaches you how to gain end-to-end observability into distributed AI applications on Azure. You start by instrumenting applications with OpenTelemetry to capture distributed traces, create custom spans, and export telemetry to Azure Monitor Application Insights. You then analyze the collected telemetry by writing KQL queries, exploring error patterns and performance trends, building dashboards and workbooks for operational visibility, and configuring alerts to detect failures and anomalies.