AI-200T00: Develop AI cloud solutions on Azure

Course structure

Module 1: Store and Manage Containers in Azure Container Registry

• 1.1 Introduction 
• 1.2 Registries, repositories, and artifacts 
• 1.3 Build and run images with ACR Tasks 
• 1.4 Tag and version images 
• 1.5 Summary 

Module 2: Deploy Containers to Azure App Service

• 2.1 Introduction 
• 2.2 Deploy containers to Azure App Service 
• 2.3 Configure container runtime behavior 
• 2.4 Configure application settings 
• 2.5 Observe and troubleshoot containerized apps 
• 2.6 Summary 

Module 3: Deploy Containers to Azure Container Apps

• 3.1 Introduction 
• 3.2 Explore Container Apps environments 
• 3.3 Deploy a container app using the Azure CLI and YAML 
• 3.4 Configure runtime settings with environment variables and secrets 
• 3.5 Configure image pull authentication for private registries 
• 3.6 Verify deployments with logs and status 
• 3.7 Summary 

Module 4: Manage containers in Azure Container Apps

• 4.1 Introduction 
• 4.2 Update images and manage revisions safely 
• 4.3 Manage the container app lifecycle 
• 4.4 Monitor logs and troubleshoot issues 
• 4.5 Configure health probes and troubleshoot failures 
• 4.6 Optimize container resources and scaling 
• 4.7 Summary 

Module 5: Scale containers in Azure Container Apps

• 5.1 Introduction 
• 5.2 Configure scale rules 
• 5.3 Implement event-driven scaling with KEDA 
• 5.4 Apply KEDA scalers for custom workloads 
• 5.5 Select compute resources for performance and cost 
• 5.6 Choose and apply revision modes 
• 5.7 Summary 

Module 6: Deploy applications to Azure Kubernetes Service

• 6.1 Introduction 
• 6.2 Create Kubernetes deployment manifests 
• 6.3 Expose applications in Azure Kubernetes Services 
• 6.4 Deploy applications to Azure Kubernetes Services 
• 6.5 Summary 

Module 7: Configure applications on Azure Kubernetes Service

• 7.1 Introduction 
• 7.2 Define ConfigMaps for application settings 
• 7.3 Implement secrets for sensitive data 
• 7.4 Attach persistent storage to an app 
• 7.5 Summary 

Module 8: Monitor and troubleshoot applications on Azure Kubernetes Service

• 8.1 Introduction 
• 8.2 Monitor application logs and metrics 
• 8.3 Troubleshoot pods and services 
• 8.4 Verify service connectivity and endpoints 
• 8.5 Summary 

Module 9: Build queries for Azure Cosmos DB for NoSQL

• 9.1 Introduction 
• 9.2 Explore Azure Cosmos DB for NoSQL 
• 9.3 Implement the Azure Cosmos DB for NoSQL SDK 
• 9.4 Query Azure Cosmos DB for NoSQL 
• 9.5 Summary 

Module 10: Implement vector search on Azure Cosmos DB for NoSQL

• 10.1 Introduction 
• 10.2 Store and retrieve embeddings in Azure Cosmos DB 
• 10.3 Execute vector similarity queries for semantic search 
• 10.4 Combine vector similarity results with metadata filtering 
• 10.5 Use the change feed to trigger embedding refresh 
• 10.6 Summary 

Module 11: Optimize query performance for Azure Cosmos DB for NoSQL

• 11.1 Introduction 
• 11.2 Understand indexes in Azure Cosmos DB 
• 11.3 Configure range and composite indexes 
• 11.4 Tune vector indexes for embedding workloads 
• 11.5 Reduce RU costs with strategic indexing 
• 11.6 Choose consistency levels for optimal performance 
• 11.7 Summary 

Module 12: Build and query with Azure Database for PostgreSQL

• 12.1 Introduction 
• 12.2 Explore Azure Database for PostgreSQL 
• 12.3 Connect to PostgreSQL 
• 12.4 Create and manage schemas 
• 12.5 Query data 
• 12.6 Integrate SDKs and applications 
• 12.7 Summary 

Module 13: Implement vector search with Azure Database for PostgreSQL

• 13.1 Introduction 
• 13.2 Store and query embeddings with pgvector 
• 13.3 Perform fast vector similarity search 
• 13.4 Manage index lifecycle and embedding updates 
• 13.5 Run vector similarity search for semantic retrieval 
• 13.6 Implement retrieval patterns for RAG pipelines 
• 13.7 Summary 

Module 14: Optimize vector search in Azure Database for PostgreSQL

• 14.1 Introduction 
• 14.2 Tune PostgreSQL for pgvector 
• 14.3 Choose and configure vector indexes 
• 14.4 Optimize data layout 
• 14.5 Scale for high-volume workloads 
• 14.6 Connection optimization 
• 14.7 Summary 

Module 15: Implement data operations in Azure Managed Redis

• 15.1 Introduction 
• 15.2 Explore Azure Managed Redis 
• 15.3 Client libraries and development best practices 
• 15.4 Implement data operations 
• 15.5 Summary 

Module 16: Implement event messaging with Azure Managed Redis

• 16.1 Introduction 
• 16.2 Publish and subscribe to events with Redis pub/sub 
• 16.3 Implement task queues with Redis Streams 
• 16.4 Choose between broadcast and coordinated distribution 
• 16.5 Summary 

Module 17: Implement vector storage in Azure Managed Redis

• 17.1 Introduction 
• 17.2 Index and query vector data 
• 17.3 Choose vector types and indexing strategies 
• 17.4 Optimize Redis data structures for vector storage 
• 17.5 Summary 

Module 18: Queue and process AI operations with Azure Service Bus

• 18.1 Introduction 
• 18.2 Explore Azure Service Bus concepts and messaging in AI architectures 
• 18.3 Choose between queues and topics with subscriptions 
• 18.4 Structure messages for AI workloads 
• 18.5 Process messages reliably 
• 18.6 Summary 

Module 19: Develop event-driven AI workflows with Azure Event Grid

• 19.1 Introduction 
• 19.2 Understand Azure Event Grid concepts and event-driven patterns for AI solutions 
• 19.3 Work with event schemas and properties 
• 19.4 Configure delivery and retry policies for reliable event processing 
• 19.5 Publish custom events from AI applications 
• 19.6 Summary 

Module 20: Build serverless AI backends with Azure Functions

• 20.1 Introduction 
• 20.2 Understand Azure Functions hosting and scaling for AI workloads 
• 20.3 Set up the local development environment for Functions 
• 20.4 Create triggers and bindings for AI integration patterns 
• 20.5 Manage secrets and configuration in Functions 
• 20.6 Configure identity and access for Functions 
• 20.7 Summary 

Module 21: Manage application secrets with Azure Key Vault

• 21.1 Introduction 
• 21.2 Store and organize secrets, keys, and certificates 
• 21.3 Retrieve secrets using Azure SDK client libraries 
• 21.4 Handle secret versioning and rotation 
• 21.5 Implement caching strategies to reduce Key Vault calls 
• 21.6 Summary 

Module 22: Manage application settings with Azure App Configuration

• 22.1 Introduction 
• 22.2 Connect to App Configuration from application code 
• 22.3 Organize settings with labels and feature flags 
• 22.4 Reference Key Vault secrets from App Configuration 
• 22.5 Decide what to store in App Configuration vs Key Vault 
• 22.6 Summary 

Module 23: Instrument an app with OpenTelemetry

• 23.1 Introduction 
• 23.2 Explore OpenTelemetry and its role in observability 
• 23.3 Add the OpenTelemetry SDK to an application 
• 23.4 Configure spans and traces 
• 23.5 Export telemetry to Azure Monitor 
• 23.6 Debug distributed flows with trace data 
• 23.7 Summary 

Module 24: Analyze app telemetry with logs and metrics

• 24.1 Introduction 
• 24.2 Write basic KQL queries 
• 24.3 Explore logs for errors and performance 
• 24.4 Build dashboards for app telemetry 
• 24.5 Create workbooks for interactive analysis 
• 24.6 Set alerts for app failures and anomalies 
• 24.7 Summary