Azure AI Fundamentals Micro-Credential

Azure AI Fundamentals Micro-Credential

Duration: 3-4 Hours

Description

For teams already building on Microsoft’s stack, Azure AI is often the shortest path from “we should use AI” to something running in production. This session is a hands-on tour of the Azure AI platform: Azure AI Foundry for building and deploying models and agents, Azure OpenAI Service, and the pre-built Cognitive Services (vision, language, speech) that solve common problems without training anything from scratch. Participants deploy a model, call it from a simple application, and wire up basic monitoring and cost controls.

Audience

This session is for developers and solution architects working in a Microsoft/.NET or Azure environment who need to add AI capability to an existing application or platform. It assumes comfort with the Azure portal but not prior AI/ML experience.

Objectives

  • Navigate the Azure AI platform and choose between Cognitive Services, Azure OpenAI Service, and custom model deployment
  • Deploy a model in Azure AI Foundry and call it from an application
  • Apply the responsible AI tooling built into the Azure platform
  • Understand cost management and scaling considerations for Azure AI workloads

Prerequisites

Basic programming experience (C# or Python) and general familiarity with the Azure portal are expected. No prior AI/ML experience is required.

Related AI Courses

See the full AI training roadmap and course directory for how this fits into a broader learning path.

Need the RAG and retrieval layer to go with this? See Beginner’s Guide to RAG Micro-Credential and Introduction to Vector Databases for Developers Micro-Credential. Building agents on top of Azure AI Foundry? See Beginner’s Guide to Agentic AI Micro-Credential.

Course Outline

Module 1: The Azure AI Landscape

  • Cognitive Services vs. Azure OpenAI Service vs. Custom Models
  • Introduction to Azure AI Foundry
  • Setting Up a Project and Resource Group
  • Lab – Call a Pre-Built Cognitive Service from Code

Module 2: Building with Azure OpenAI Service

  • Deploying a Model in Azure AI Foundry
  • Calling the Model from a .NET or Python Application
  • Grounding Responses with Your Own Data
  • Lab – Deploy and Query a Model End-to-End

Module 3: Running It Responsibly and at Scale

  • Built-In Content Filtering and Responsible AI Tooling
  • Monitoring, Logging, and Cost Management
  • Scaling Considerations for Production Workloads
  • Lab – Add Monitoring and a Cost Alert to a Deployed Model