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AI-200: Azure AI Cloud Developer Associate - Complete Course
Hot & New
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Rating: 4.5 out of 5(28 ratings)
212 students

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

Master Production-Ready AI Cloud Development: Scale multi-agent systems, secure vector data, and deploy enterprise AI
Hot & New
New
Created byLuke Ginn
Last updated 6/2026
English

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

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Course content

3 sections • 30 lectures • 10h 9m total length

  • Unit 1: The Absolute Basics – What Is an AI Integration Engineer?20:30
  • Unit 2: Storing Containers – Azure Container Registry (ACR)20:41
  • Unit 3: Serverless Containers – Azure Container Apps18:15
  • Unit 4: Full Orchestration – Azure Kubernetes Service (AKS)21:33
  • Unit 5: The AI Memory Problem – Introduction to Vector Search20:57
  • Unit 6: Globally Distributed Vector Search – Cosmos DB for NoSQL20:52
  • Unit 7: Relational Vector Search – PostgreSQL with pgvector18:15
  • Unit 8: Ultra-Fast Vector Search – Azure Managed Redis19:44
  • Unit 9: Decoupling for Resilience – Azure Service Bus19:36
  • Unit 10: Reactive AI – Azure Event Grid for Event-Driven Workflows19:00
  • Unit 11: Serverless Glue – Azure Functions for AI Backends17:00
  • Unit 12: Securing Credentials – Azure Key Vault for Zero-Trust Secrets18:06
  • Unit 13: Configuration as Control Plane – Azure App Configuration16:25
  • Unit 14: Observability Problem – Why Logs Aren't Enough16:30
  • Unit 15: Analyzing Telemetry – KQL and Application Insights17:23
  • Unit 16: Caching for Latency – Azure Managed Redis (Data Operations)18:40
  • Unit 17: Real-Time Coordination – Redis Pub/Sub and Streams17:41
  • Unit 18: Bringing It Together – Integration Architecture Review18:47
  • Unit 19: Production Hardening – Security, Reliability, Cost24:09
  • Unit 20: Capstone – Design a Production-Ready AI Integration19:59
  • Disclaimer0:23

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

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