• Microsoft Azure
  • Data and AI (Azure)

Develop AI cloud solutions on Azure (AI-200T00)

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Learning Track icon
Learning Track

Data and AI (Azure)

Delivery methods icon
Delivery methods

On-Site, Virtual

Duration icon
Duration

5 days

This course teaches developers how to create, monitor, and troubleshoot AI solutions on Microsoft Azure. Students will learn how to implement Azure compute and containerization patterns to host applications, build serverless APIs with Azure Functions, and integrate services using event‑driven and message‑based architectures such as Azure Service Bus and Event Grid. The course also covers working with Azure data services that support AI workloads, including designing and querying solutions with Cosmos DB for NoSQL, Azure Database for PostgreSQL with pgvector, and Azure Managed Redis for caching, streaming, and vector search. By the end of the course, developers will be able to connect services, orchestrate AI workflows, and build secure, scalable, and observable AI‑driven applications on Azure. 

Learning Objectives

  • Deploy and configure containerized microservices using Azure Container Apps and Azure Kubernetes Service
  • Implement high-dimensional vector search capabilities using Azure Cosmos DB, Azure
  • Database for PostgreSQL, and Azure Managed Redis
  • Build decoupled, event-driven architectures utilizing Azure Service Bus, Event Grid, and serverless Azure Functions
  • Secure application settings and credentials using Azure Key Vault and App Configuration
  • Instrument applications with OpenTelemetry to collect distributed traces and metrics
  • Analyze application performance and telemetry data using Application Insights and Azure Monitor
  • Create proactive alerting dashboards to monitor the health and performance of distributed workloads
     

Who Should Attend

Developers who build backend and AI-driven applications on Azure and need practical skills in
  • Containerized compute
  • Data services for AI
  • Event-driven workflows
  • Application security
  • Monitoring and diagnostics

     

Prerequisites

  • Programming experience with a language such as Python, JavaScript, or C#.
  • A basic understanding of Azure services and cloud computing concepts.
  • Familiarity with containerization concepts, including Docker and Kubernetes fundamentals.
  • Familiarity with relational databases, JSON document structures, and SQL fundamentals.
  • An understanding of AI and machine learning concepts like embeddings and vector similarity search.

 

Activities

This course includes presentations based on use cases. It also includes group discussions and demonstrations.

Course outline

  • The AI business strategist's role

  • The Enterprise Strategy Layer

  • The 3D framework

  • Technical Foundations: What the AI Business Strategist Needs to Know

  • Activity: Scenario Triage

  • Diagnose the Customer's Actual Problem

  • Assess Organizational Readiness

  • Establish Data Foundation

  • Activity: Discovery

  • Map Business Problems to AI Capabilities

  • Weave in Governance and Responsible AI

  • Specify POC scope and pilot success criteria

  • Activity: Governance in a Regulated Architecture

  • Build, calibrate, and evaluate the POC

  • Deploy bounded pilot and lead change

  • Scale from Pilot to Enterprise and Connect to Strategy

  • Activity: Deliver

  • Multi-Agent Architectures

  • Autonomy vs. Control in Depth

  • Generation, Evaluation, and Production

  • Activity: Break the Agent

  • Use Case Introduction and Framework Review

  • Simulation Debrief

  • Map Pain Points to AI Capabilities

  • Governance by Design

  • POC scope and pilot success criteria

  • Build, calibrate, and evaluate the POC

  • Deploy bounded pilot and lead change

  • Scale to enterprise platform

  • Next Steps for Continued Learning

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