Course
digicode: AI200
Develop AI Cloud Solutions on Microsoft Azure – Intensive Training (AI-200)
AI-200
Course facts
Download as PDF- Explaining the container lifecycle using Azure Container Registry (ACR), App Service, and Container Apps
- Securing configurations with Kubernetes primitives (ConfigMaps, Secrets) and Azure Key Vault
- Implementing vector search across Azure Cosmos DB, PostgreSQL (with pgvector), and Managed Redis
- Optimizing container scaling and resource allocation using scale rules and KEDA scalers
- Decoupling AI components using Azure Service Bus (queues/topics) and reliable task queues with Redis Streams
- Building event-driven architectures with Azure Event Grid and the CloudEvents schema
- Troubleshooting distributed AI workloads by configuring OpenTelemetry for Azure Monitor Application Insights and KQL analysis
- Connecting applications securely using Microsoft Entra authentication and Key Vault/App Configuration secret management
1 Implement container application hosting on Azure
Explore the core container hosting workflows on Azure, including image management with Azure Container Registry and custom container deployment to Azure App Service with runtime configuration.
2 Deploy and manage apps on Azure Container Apps
This module covers the complete lifecycle of containerized applications on Azure Container Apps, including deployment, configuration, revision management, and configuring automatic horizontal scaling.
3 Deploy and monitor applications on Azure Kubernetes Service
A guide through the complete AKS lifecycle, covering deployment with manifests and services, externalizing configuration with ConfigMaps and Secrets, attaching persistent storage, and monitoring application health.
4 Develop AI solutions with Azure Cosmos DB for NoSQL
Focus on developing AI solutions using Azure Cosmos DB for NoSQL by building a data foundation, implementing vector search capabilities, and optimizing query performance.
5 Develop AI solutions with Azure Database for PostgreSQL
This module guides you through developing AI solutions with Azure Database for PostgreSQL by building a data foundation, implementing vector search using the pgvector extension, and optimizing performance.
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.
7 Integrate backend services for AI solutionsIntegrate backend services like Azure Service Bus, Azure Event Grid, and Azure Functions to build reliable, event-driven, serverless AI solutions on Azure.
8 Manage application secrets and configuration for AI solutions
Learn how to securely manage secrets using Azure Key Vault and centralize application configuration, including feature flags, with Azure App Configuration.
9 Observe and troubleshoot apps on Azure
The final module teaches you how to gain end-to-end observability into distributed AI applications on Azure by instrumenting with OpenTelemetry, exporting telemetry to Application Insights, and analyzing data using KQL queries and alerts.
Component of the following courses
- Develop AI Cloud Solutions on Microsoft Azure – Intensive Training
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.
- Programming experience with languages such as Python, JavaScript, or C#
- Basic understanding of Azure services and cloud computing concepts
- Familiarity with containerization fundamentals