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AI-200: Azure AI Cloud Developer Associate - Complete Course

Name: AI-200: Azure AI Cloud Developer Associate - Complete Course
Rating: 4.5 (145 reviews)

Master Production-Ready AI Cloud Development: Scale multi-agent systems, secure vector data, and deploy enterprise AI

Created byLuke Ginn

Last updated 10/2026

English

English [Auto],

What you'll learn

Build Production-Grade AI Architectures: Go beyond agent logic to design and deploy scalable solutions using containers (Container Apps, AKS), serverless functi
Master Vector Data on Azure: Gain deep, hands-on experience with Azure's AI-optimized data stack, including vector search implementation in Azure Cosmos DB, Azu
Reinforce and Extend AI-103 Agentic Skills: Strengthen your understanding of multi-agent orchestration, grounding, and memory from AI-103 while learning to inte
Implement Enterprise Security & Observability: Learn to secure AI applications with Entra ID and managed identities, and monitor them using OpenTelemetry and Az

Course content

13 sections • 56 lectures • 21h 43m total length

Theory Unit 1: The Absolute Basics – What Is an AI Integration Engineer?20:30
Learn how to build productionized AI systems on Azure by integrating containers, vectors, and events, connecting services like container registry, Kubernetes, Cosmos DB, and Service Bus.
Quiz 1: The Absolute Basics – What Is an AI Integration Engineer?
Code Unit 1 Code Walkthrough: Foundation – Your First Agent Call38:15
Essential Guide to Course Resources & Exercises1:23
Theory Unit 2: Storing Containers – Azure Container Registry (ACR)20:41
Discover how Azure container registry provides private storage for container images, explains registry hierarchy of registries, repositories, and artifacts, and guides secure tagging with immutable, semantic versioning and environment tags.
Quiz 2: Storing Containers – Azure Container Registry (ACR)
Theory Unit 3: Serverless Containers – Azure Container Apps18:15
Run container images from azure container registry as serverless container apps on a managed platform. Explore autoscaling with keda, revisions, environment variables, secrets, health probes, and yaml-based infrastructure.
Quiz 3: Serverless Containers – Azure Container Apps
Read Before You Start Running Code: Deployment & Model Notes1:20
Special Unit: Bicep Deployment, Tips, Tricks & Advice49:20
Code Unit 2: Azure Container Apps with Hello World FastAPI21:52
Code Unit 3: Azure Container Apps with Application Factory FastAPI29:26
Learn the FastAPI application factory pattern with createApp, modular endpoints, Pydantic validation, and lifespan management, including health checks and deploying to Azure Container Registry.
Code Unit 4: Azure Container Apps for AI Agents31:50
Build an async FastAPI OpenAI agent API using the application factory pattern with startup lifecycles and config-driven settings. Expose /run for chat completions and /health for liveness and readiness probes.
Code Unit 5: Azure Container Apps and HTTP Scaling18:33
Scale a containerized OpenAI agent with fast api on Azure Container Apps using http based auto scaling, with min and max replicas and concurrency.
Code Unit 6: Azure Container Apps and CPU Scaling9:05
Code Unit 7: Azure Container Apps and Health Probes29:22

Theory Unit 4: Full Orchestration – Azure Kubernetes Service (AKS)21:33
Learn how Azure Kubernetes Service enables GPU, stateful storage, and advanced networking for AI workloads, with deployment manifests, kubectl, config maps, secrets, and persistent volumes; master scaling and troubleshooting.
Quiz 4: Full Orchestration – Azure Kubernetes Service (AKS)
Code Unit 8: Azure Kubernetes for AI Agents29:52
Learn to deploy a containerized app to Azure Kubernetes using deployments, services, and secrets, with kubectl, a load balancer, and an external IP, plus secret management for OpenAI credentials.
Code Unit 9: Azure Kubernetes and CPU Scaling17:17
Explore Kubernetes auto-scaling by configuring horizontal pod auto-scaler and cluster auto-scaler to automatically adjust pods and nodes based on CPU utilization, balancing performance and costs.
Code Unit 10: Azure Kubernetes and Health Probes21:36
Configure Kubernetes liveness and readiness probes with HTTP health endpoints in a manifest. See how probes restart or remove pods in the Kubernetes lifecycle and test them using sabotage endpoints.

Theory Unit 5: The AI Memory Problem – Introduction to Vector Search20:57
Introduce the memory layer with retrieval augmented generation, using embeddings for semantic retrieval to enrich private data answers; cover vector search, similarity metrics, and Azure vector stores.
Quiz 5: The AI Memory Problem – Introduction to Vector Search
Theory Unit 6: Globally Distributed Vector Search – Cosmos DB for NoSQL20:52
Explore Azure Cosmos DB for NoSQL, with global distribution and built-in vector search using embeddings and the vector distance function; learn its four layers, indexing, and the Python SDK.
Quiz 6: Globally Distributed Vector Search – Cosmos DB for NoSQL
Code Unit 11: Cosmos DB Vector Search and Data Processing Pipeline1:05:25
Learn how to implement Cosmos DB vector search and design a data processing pipeline for scalable AI workloads within the Azure AI Cloud Developer framework.
Code Unit 12: Cosmos DB Vector Search for AI Agents26:30
Extend Azure Cosmos DB vector search into a fast API by integrating Azure Cosmos DB vector search with OpenAI to deliver semantic search and memory-enabled results.

Theory Unit 7: Relational Vector Search – PostgreSQL with pgvector18:15
Quiz 7: Relational Vector Search – PostgreSQL with pgvector
Code Unit 13: PostgresSQL Vector Search and Data Processing Pipeline32:53
Build a Postgres SQL vector search pipeline using the pgvector extension, deploying a flexible server, generating embeddings with Azure OpenAI, and performing vector, text, and hybrid searches on PDF data.
Code Unit 14: PostgresSQL Vector Search for AI Agents29:46
Extend FastAPI to use PostgreSQL vector search with pgvector for Azure. Build a semantic search API backed by a document knowledge base, using asynchronous connection pooling and lifecycle management.

Theory Unit 8: Ultra-Fast Vector Search – Azure Managed Redis19:44
Explore Azure managed Redis with the Redis Stack for ultra-fast vector search, embedding indexing, and k nearest neighbors queries using ft.create, ft.search, and JSON.set in ram.
Quiz 8: Ultra-Fast Vector Search – Azure Managed Redis
Code Unit 15: Redis Vector Search and Data Processing Pipeline30:51
Code Unit 16: Redis Vector Search for AI Agents24:49
Extend a fast api with Azure Redis vector search to serve semantic search results from Redis stored documents, using asynchronous connections, lifespan context, and vector embeddings.

Theory Unit 9: Decoupling for Resilience – Azure Service Bus19:36
Explore how Azure Service Bus decouples ai workloads with queues and topics, enabling asynchronous messaging and reliable delivery to handle traffic bursts.
Quiz 9: Decoupling for Resilience – Azure Service Bus
Theory Unit 10: Reactive AI – Azure Event Grid for Event-Driven Workflows19:00
Quiz 10: Reactive AI – Azure Event Grid for Event-Driven Workflows
Theory Unit 11: Serverless Glue – Azure Functions for AI Backends17:00
Explore Azure Functions as serverless code triggered by HTTP, Service Bus, or Event Grid, using bindings to route data to Cosmos DB, blob storage, or other services.
Quiz 11: Serverless Glue – Azure Functions for AI Backends
Code Unit 17: Azure Event Grid for AI Agents36:36
Code Unit 18: Azure Event Grid and Azure Functions for AI Agents37:05
Use Azure Functions as a transformer between Event Grid and a fast API, converting events to HTTP requests while keeping the API unchanged. Learn bindings, environment variables, and deployment.
Code Unit 19: Azure Service Bus and Azure Functions for AI Agents40:09

Theory Unit 12: Securing Credentials – Azure Key Vault for Zero-Trust Secrets18:06
Learn how Azure Key Vault securely stores secrets, keys, and certificates, enables managed identities and role-based access control, and supports versioning, rotation, caching, and app configuration integration.
Quiz 12: Securing Credentials – Azure Key Vault for Zero-Trust Secrets
Theory Unit 13: Configuration as Control Plane – Azure App Configuration16:25
Discover Azure App Configuration as a centralized service for key value pairs and feature flags, enabling environment-specific values and no restart toggles.
Quiz 13: Configuration as Control Plane – Azure App Configuration
Code Unit 20: Azure Container Apps with Azure Key Vault23:39
Important Update: New Code Walkthroughs & Videos Coming Soon!0:40

Theory Unit 14: Observability Problem – Why Logs Aren't Enough16:30
Learn why logs alone fail in distributed AI workflows and how open telemetry tracing with spans, correlation IDs, and both automatic and manual instrumentation provide end-to-end observability.
Quiz 14: Observability Problem – Why Logs Aren't Enough
Theory Unit 15: Analyzing Telemetry – KQL and Application Insights17:23
Query Azure telemetry with kql in Application Insights to analyze traces, logs, requests, and dependencies, then build dashboards with workbooks and alerts, including ai-focused kql queries for embeddings and llm.
Quiz 15: Analyzing Telemetry – KQL and Application Insights
Important Update: New Code Walkthroughs & Videos Coming Soon!0:40

Theory Unit 18: Bringing It Together – Integration Architecture Review18:47
Develop and compare synchronous rag api and asynchronous batch inference architectures on Azure AI cloud. Link container apps, Cosmos DB, Redis, Service Bus, Event Grid, Kubernetes, and OpenTelemetry for monitoring.
Quiz 18: Bringing It Together – Integration Architecture Review
Theory Unit 19: Production Hardening – Security, Reliability, Cost24:09
Master security, reliability, and cost in production projects by implementing private endpoints and vnet isolation, managed identities, auditing, retry policies, circuit breakers, geo failure planning, and budget‑driven governance.
Quiz 19: Production Hardening – Security, Reliability, Cost
Theory Unit 20: Capstone – Design a Production-Ready AI Integration19:59
Quiz 20: Capstone – Design a Production-Ready AI Integration

Requirements

Basic Azure & Python Proficiency: You should be comfortable with the Azure portal, basic cloud concepts, and Python programming (APIs, SDKs). Experience with AZ-204 level concepts (like Functions or Logic Apps) is helpful but not strictly required, as we cover the specific AI-200 topics from the ground up
Completed AI-103 (or Equivalent Experience): This course assumes you have working knowledge of building agents, implementing RAG, and using the Azure AI Foundry Agent Service. We will not re-teach basic agent creation; instead, we focus on scaling and securing those agents for the cloud

Description

AI-200: Azure AI Cloud Developer Associate – Complete Course

Key Benefits

Production-Scale Implementation: Go beyond agent logic with comprehensive lectures and hands-on code walkthroughs covering Kubernetes deployment, Cosmos DB vector search, multi-agent scaling, and Entra ID security.
Architectural Mastery: Understand the 'why' behind cloud AI patterns with deep dives into Azure Container Apps, AKS, and enterprise vector databases from official documentation.
Build Real-World Solutions: Move from concept to infrastructure by building deployable AI systems that leverage Kubernetes orchestration, Cosmos DB for grounding, and secure managed identities.
Pass the AI-200 Confidently: Complement your exam practice with the technical depth and practical experience required to master Microsoft's latest AI-200 certification.

Are you ready to lead the 2026 shift from agent prototypes to production cloud AI?

The AI-200 exam is challenging because it requires more than just building agents—it requires the ability to architect, secure, and scale complex AI systems across enterprise cloud infrastructure. This course is designed to take you from the foundations of Azure AI Foundry to the cutting edge of multi-agent scaling, vector data management, and Kubernetes deployment.

Updated for the latest 2026 syllabus, this lecture-based course provides the technical "missing link" between agent development and production deployment. While our AI-103 course teaches you how to build agents, this course teaches you how to deploy, secure, and scale them for real enterprise workloads.

What You Will Build and Master:

Through detailed modules and technical walkthroughs, you will cover every objective domain tested on the AI-200 exam:

Kubernetes for AI Workloads: Deploy multi-agent systems to Azure Kubernetes Service (AKS) and Container Apps with auto-scaling, health probes, and rolling updates.
Vector Data with Cosmos DB: Implement production grounding using Azure Cosmos DB for NoSQL with vector search, including indexing strategies, partitioning, and hybrid search patterns.
Enterprise Security & Identity: Secure all AI services using Entra ID managed identities, role-based access control (RBAC), and key vault integration for secrets management.
Event-Driven Multi-Agent Architectures: Build asynchronous agent workflows using Azure Service Bus, Event Grid, and Durable Functions to coordinate distributed agent handoffs at scale.
Cloud Observability: Implement OpenTelemetry for agent tracing, configure Azure Monitor for performance alerts, and debug production agent failures with Application Insights.
Storage for Agent Memory: Persist agent state across sessions using Azure Cosmos DB, Azure Redis Cache, and Blob Storage with proper consistency and throughput planning.

How This Course Will Get You Certified

Deep Technical Lectures: We break down complex cloud AI patterns into digestible, visual lessons so you understand the logic behind production-grade multi-agent systems.
Code-First Approach: Every major concept is accompanied by a code walkthrough, ensuring you can deploy Entra-secured Cosmos DB, configure AKS clusters, and scale multi-agent systems in your own environment.
Bridge the Gap: This course is the perfect companion to the AI-103 course. Learn agent building there. Learn production scaling here.
Future-Proof Your Career: Focus on the latest 2026 standards for cloud AI development, moving beyond isolated agents into enterprise-scale, secure, observable AI systems.

This Course Is Perfect For:

AI-103 Graduates seeking AI-200 certification who want to master Kubernetes, Cosmos DB, and enterprise observability through technical deep-dives.
Cloud Developers building production AI solutions on Microsoft Azure who need to understand infrastructure, security, and scaling patterns for multi-agent systems.

Requirements

Completed AI-103 (or equivalent agent-building experience): This course assumes you know how to build agents, implement grounding, and use the Azure AI Foundry Agent Service. We focus on scaling and securing those agents for the cloud.
Basic Azure & Infrastructure Familiarity: You should be comfortable with the Azure portal, resource groups, and basic CLI usage. Experience with containers or Kubernetes is helpful but not required—we cover AI-200 topics from the ground up.

Who this course is for:

Cloud Developers (AZ-204 holders) Transitioning to AI: You hold AZ-204 or have experience with Azure compute/storage, but you need to modernize your skills. You want to learn how to take traditional cloud apps and integrate cutting-edge AI features (agents, semantic search, orchestration) without losing the "cloud" best practices for scaling and security
AI-103 Graduates Seeking the Next Certification: You have your AI-103 (or equivalent agent-building skills) and want to earn the Microsoft Certified: Azure AI Cloud Developer Associate certification. You are ready to learn the containerization, vector DB, and observability skills that differentiate a junior agent builder from a cloud AI developer

AI-200: Azure AI Cloud Developer Associate - Complete Course

What you'll learn

Explore related topics

Course content

Introduction | Azure Container Apps | FastAPI13 lectures • 4hr 50min

Azure Kubernetes4 lectures • 1hr 30min

Azure Cosmos DB4 lectures • 2hr 14min

Azure PostgresSQL3 lectures • 1hr 21min

Azure Redis3 lectures • 1hr 15min

Azure Event Grid | Azure ServiceBus | Azure Functions6 lectures • 2hr 49min

Azure Key Vault | Azure App Configuration4 lectures • 59min

Azure Monitor | Azure Application Insights3 lectures • 35min

Azure Redis for Caching3 lectures • 37min

Finishing up3 lectures • 1hr 3min

Requirements

Description

Who this course is for: