The AI-200T00-A course focuses on developing AI-powered cloud solutions using Microsoft Azure services and modern application architectures. Learners will explore how to build scalable AI-driven applications using Azure compute services, containerized workloads, serverless APIs, and event-driven integrations. The course also covers the implementation of intelligent workflows using Azure Service Bus, Event Grid, and Azure Functions for enterprise-grade AI application orchestration.  

Participants will gain practical exposure to Azure data services that support AI workloads, including Cosmos DB for NoSQL, Azure Database for PostgreSQL with pgvector, and Azure Managed Redis for vector search, streaming, and caching scenarios. Through hands-on activities and guided demonstrations, learners will understand how to design secure, scalable, observable, and production-ready AI cloud applications using Microsoft Azure technologies. 

By the end of the course, learners will be able to connect Azure services, orchestrate AI workflows, implement secure architectures, and develop enterprise AI applications capable of supporting modern intelligent business solutions. 

After completing Develop AI Cloud Solutions on Microsoft Azure, students will be able to:

  • Describe Azure AI cloud solution architecture and core Azure AI services.
  • Implement Azure compute and containerization patterns for AI applications.
  • Build serverless APIs using Azure Functions.
  • Design event-driven and message-based integration architectures.
  • Use Azure Service Bus and Event Grid for AI workflow orchestration.
  • Implement Cosmos DB for NoSQL in AI-driven applications.
  • Configure Azure Database for PostgreSQL with pgvector for vector-based workloads.
  • Use Azure Managed Redis for caching, streaming, and vector search scenarios.
  • Monitor, troubleshoot, and optimize AI applications on Azure.
  • Build secure, scalable, and observable AI cloud solutions.

Upcoming Batches

Loading Dates...

Key Features of Develop AI Cloud Solutions on Microsoft Azure

  • Hands-on Labs and Activities: More than 50% of the course focuses on practical Azure AI implementation and integration exercises. 

  • Real-World AI Cloud Architectures: Learners work with enterprise-grade AI application scenarios using Azure-native services. 

  • Expert-Led Training Sessions: Delivered by Microsoft-certified trainers experienced in Azure AI, cloud-native development, and enterprise architectures. 

  • Modern AI Application Development: Covers serverless APIs, event-driven workflows, containerization, and AI orchestration techniques. 

  • Data Services for AI Workloads: Includes practical usage of Cosmos DB, PostgreSQL with pgvector, and Azure Managed Redis. 

  • Cloud-Native Development Focus: Learn scalable and observable AI application deployment and monitoring practices. 

  • Industry-Aligned Curriculum: Course content follows official Microsoft Learn AI-200T00-A learning objectives. 

  • Enterprise Security and Monitoring: Understand secure AI application deployment, observability, and troubleshooting approaches. 

Who Should Attend:

  • Azure Developers
  • AI Engineers and Application Developers
  • Cloud Solution Architects
  • Backend Developers Building AI-Driven Applications

Prerequisites of AI 200

  • Basic understanding of Microsoft Azure services and cloud concepts.
  • Experience with application development or backend programming.
  • Familiarity with APIs, databases, and event-driven architectures is beneficial.
  • Basic understanding of containers and serverless computing concepts.
  • Interest in developing AI-powered cloud-native applications.

Why choose CloudThat as your training partner?

  • Expert Azure AI Trainers – Learn from Microsoft-certified professionals experienced in enterprise AI cloud application development and Azure architectures. 
  • Hands-On Learning Approach – Participate in guided labs and implementation-focused exercises designed for real-world AI solution development. 
  • Enterprise AI Use Cases – Practice scalable AI application scenarios involving APIs, messaging, vector databases, and event-driven integrations. 
  • Flexible Training Delivery – Attend instructor-led online, classroom, corporate, or customized training sessions according to organizational requirements. 
  • Comprehensive Lab Support – Access guided exercises, architecture references, deployment examples, and post-training learning resources. 
  • Industry-Aligned Curriculum – Training content closely follows Microsoft Learn AI-200T00-A official course objectives and enterprise AI trends. 
  • Focus on Cloud-Native AI Development – Gain practical skills in scalable, observable, and secure AI solution implementation on Azure. 

Download Course Outline

  • Build, store, version, and manage container images by using Azure Container Registry
  • Build and run images by using Azure Container Registry Tasks

  • Deploy applications to Azure Container Apps, including environment configuration and revision management

  • Implement event-driven scaling by using Kubernetes Event‑driven Autoscaling (KEDA) in Container Apps
  • Deploy and manage applications to Azure Kubernetes Service (AKS) by using manifest files
  • Monitor and troubleshoot solutions on AKS and Container Apps by inspecting logs, events, and end-to-end connectivity

  • Connect to Azure Cosmos DB for NoSQL by using the SDK and run queries
  • Optimize query performance and Request Units (RUs) consumption by using indexing policies and consistency levels
  • Store and retrieve embeddings and execute vector similarity search for semantic retrieval
  • Implement a change feed processor to detect and handle new or updated items

  • Connect and query Azure Database for PostgreSQL by using SDKs
  • Model schemas and implement indexing strategies, including designing tables and choosing appropriate data types
  • Implement indexing strategies, including optimizing query latency and reducing pgvector compute overhead
  • Configure compute, memory, and storage resources to support vector workloads
  • Run vector similarity search, including storing embeddings, semantic retrieval, and implementing retrieval-augmented generation (RAG) patterns by using metadata filter
  • Implement connection optimization to improve throughput and minimize latency

  • Implement Azure Managed Redis data operations, including caching, expiration, and invalidation
  • Implement vector indexing to enable similarity search

  • Queue and process back-end operations by using Azure Service Bus, including dead-letter queue handling, messages, topics, and subscriptions
  • Implement event-driven workflows by using Azure Event Grid, including filters, custom events, and retries
  • Build serverless APIs, including implementing triggers and bindings
  • Configure and deploy function apps

  • Secure secrets by using Azure Key Vault, including rotation and retrieval
  • Store and retrieve app configuration information by using Azure App Configuration

  • Trace distributed systems by using Open Telemetry SDKs
  • Write KQL queries to analyze logs and metrics

Certification details of Develop AI Cloud Solutions on Microsoft Azure

  • certification: Microsoft Certified: Developing AI Cloud Solutions on Azure(Exam: AI-200)
  • Focus: On developing AI-powered cloud solutions on Microsoft Azure using modern cloud-native and AI integration patterns
  • Validity: 1 year, renewable through Microsoft’s free annual renewal assessment.

Click to Zoom

Select Course date

Loading Dates...
Add to Wishlist

Course ID: 28871

Course Price at

Loading price info...
Enroll Now

FAQs

The AI-200T00-A course teaches developers how to build, monitor, troubleshoot, and integrate AI cloud solutions using Microsoft Azure services.

Azure developers, AI engineers, backend developers, and cloud architects building AI-powered applications can benefit from this course.

The course is designed for intermediate-level learners with basic Azure and application development knowledge.

The course covers Azure Functions, Cosmos DB, Azure Service Bus, Event Grid, PostgreSQL with pgvector, Azure Managed Redis, and containerized compute services.

Yes. Learners participate in guided labs and implementation exercises focused on building AI cloud solutions using Azure services.

You will learn AI cloud architecture, serverless APIs, event-driven integration, vector databases, monitoring, and scalable AI application development.

PostgreSQL with pgvector enables vector storage and similarity search capabilities commonly used in generative AI and semantic search applications.

These services enable event-driven communication, workflow orchestration, and reliable message-based integration between AI application components.

The course helps professionals build practical Azure AI cloud development skills increasingly required in modern AI and cloud engineering roles.

Professionals skilled in Azure AI cloud development, vector databases, and scalable AI application architectures are highly valued in enterprise digital transformation initiatives.

Enquire Now