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AI-200T00: Develop AI cloud solutions on Azure
Build AI applications on Azure that are scalable, secure and ready for production. This intermediate, self-paced course for developers covers containerised hosting with Azure Container Apps and Kubernetes, vector search with Cosmos DB, PostgreSQL and Redis, event-driven and serverless backends, secrets management and monitoring. It prepares you for the Microsoft Certified: Azure AI Cloud Developer Associate (AI-200) exam.
Available in Instructor-Led or Virtual formats.
Duration
40 Hours | 5 Days
Modules
23 Modules
Course type
Microsoft
Course Overview
This course equips learners to design, deploy, integrate, secure, and observe AI-enabled applications on Microsoft Azure. The course covers containerized and serverless architectures, Azure Kubernetes Service, Azure Container Apps, Azure Container Registry, vector search technologies, caching, event-driven integration, and application observability. Learners work with Azure Cosmos DB, PostgreSQL, Redis, Event Grid, Service Bus, Azure Functions, Key Vault, App Configuration, and OpenTelemetry. By the end, participants can build scalable, resilient, secure, and production-ready AI solutions across modern cloud environments.
Before You Begin
- Cloud and Container Fundamentals
- Data and Messaging Basics
- Programming and Application Development Skills
Key outcomes
- Store, manage, and secure container images using Azure Container Registry.
- Deploy containerized applications to Azure App Service, Azure Container Apps, and Azure Kubernetes Service.
- Configure scaling, networking, ingress, revisions, secrets, identities, and runtime settings.
- Build AI-enabled data layers using Azure Cosmos DB, PostgreSQL, and Redis.
- Implement vector search solutions for AI workloads.
- Optimize database performance using indexing, partitioning, and query tuning.
- Secure secrets and configuration using Key Vault and App Configuration.
- Analyze telemetry, logs, metrics, and diagnostics to improve reliability and performance.
- Design resilient, scalable, and production-ready AI solutions on Azure.
Tools you’ll use
Azure Container Registry
Azure Kubernetes Service (AKS)
Docker
Helm
Azure Container Apps
OpenTelemetry
Skills you’ll gain
- Manage container images and repositories
- Deploy applications using Azure Container Apps
- Configure and manage Azure Kubernetes Service workloads
- Build AI solutions with Azure Cosmos DB
- Build AI applications with Azure Database for PostgreSQL
- Implement caching and event messaging using Redis
- Design event-driven architectures
- Deploy scalable AI-enabled cloud applications
What You’ll Learn
23 modules
1Module 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
1.2 Registries, repositories, and artifacts
1.3 Build and run images with ACR Tasks
1.4 Tag and version images
1.5 Summary
2Module 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
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
3Module 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
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
4Module 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
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
5Module 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
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
6Module 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
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
7Module 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
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
8Module 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
8.2 Monitor application logs and metrics
8.3 Troubleshoot pods and services
8.4 Verify service connectivity and endpoints
8.5 Summary
9Module 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
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
10Module 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
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
11Module 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
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
12Module 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
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
13Module 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
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
14Module 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
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
15Module 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
15.2 Explore Azure Managed Redis
15.3 Client libraries and development best practices
15.4 Implement data operations
15.5 Summary
16Module 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
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
17Module 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
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
18Module 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
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
19Module 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
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
20Module 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
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
21Module 21: Manage application settings with Azure App Configuration
21.1 Introduction
21.2 Connect to App Configuration from application code
21.3 Organize settings with labels and feature flags
21.4 Reference Key Vault secrets from App Configuration
21.5 Decide what to store in App Configuration vs Key Vault
21.6 Summary
21.2 Connect to App Configuration from application code
21.3 Organize settings with labels and feature flags
21.4 Reference Key Vault secrets from App Configuration
21.5 Decide what to store in App Configuration vs Key Vault
21.6 Summary
22Module 22: Instrument an app with OpenTelemetry
22.1 Introduction
22.2 Explore OpenTelemetry and its role in observability
22.3 Add the OpenTelemetry SDK to an application
22.4 Configure spans and traces
22.5 Export telemetry to Azure Monitor
22.6 Debug distributed flows with trace data
22.7 Summary
22.2 Explore OpenTelemetry and its role in observability
22.3 Add the OpenTelemetry SDK to an application
22.4 Configure spans and traces
22.5 Export telemetry to Azure Monitor
22.6 Debug distributed flows with trace data
22.7 Summary
23Module 23: Analyze app telemetry with logs and metrics
23.1 Introduction
23.2 Write basic KQL queries
23.3 Explore logs for errors and performance
23.4 Build dashboards for app telemetry
23.5 Create workbooks for interactive analysis
23.6 Set alerts for app failures and anomalies
23.7 Summary
23.2 Write basic KQL queries
23.3 Explore logs for errors and performance
23.4 Build dashboards for app telemetry
23.5 Create workbooks for interactive analysis
23.6 Set alerts for app failures and anomalies
23.7 Summary
Certification & Credentials
Certificate track
40 Hours | 5 Days
High-Quality Video Lessons
Digital courseware, e-books & audio access
Recorded sessions, accessible on tablet & phone
AI mentor support & module quizzes
Learning Resources
Videos & Engaging Visual Content
Podcasts & Audiobooks
Expert discussions and audio episodes covering key topics
E-Books
Downloadable guides
Module-Wise Quizzes
Interactive quizzes after each module to reinforce learning.
Additional Resources
Study materials, exam prep guides, sandbox labs, and community tutorials.
Frequently asked questions
Does the course cover containers and Kubernetes?
Yes. The course covers Azure Container Registry, Azure Container Apps, Azure App Service, and Azure Kubernetes Service.
What vector databases are included?
Azure Cosmos DB for NoSQL, Azure Database for PostgreSQL, and Azure Managed Redis support vector search capabilities.
Does the course include event-driven architecture?
Yes. Azure Service Bus, Event Grid, and Azure Functions are covered for backend integration and asynchronous processing.
Is this course suitable for developers?
Yes. The course is intended for learners with programming experience in languages such as C#, Java, Python, or JavaScript.
What security services are included?
Azure Key Vault and Azure App Configuration are used to manage secrets, keys, certificates, and application settings securely.
| Duration | 40 Hours / 5 Days |
|---|---|
| Modules | 23 Modules |
| Course type | Microsoft |
| Category | Microsoft |
| Prerequisites | Cloud and Container Fundamentals, Data and Messaging Basics, Programming and Application Development Skills |
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