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What You'll Learn

Design complete enterprise Generative AI architectures on Microsoft Azure.,Build secure RAG applications with Azure OpenAI and Azure AI Search.,Connect enterprise data from SQL
Cosmos DB
Blob Storage
SharePoint
and APIs.,Create ingestion
document processing
chunking
embedding
and indexing pipelines.,Build AI agents that use tools
memory
workflows
and human approvals.,Apply prompt protection
content safety
identity
access
and data guardrails.,Select Azure App Service
Container Apps
or Functions for each AI workload.,Monitor latency
token usage
retrieval quality
failures
and safety events.,Implement CI/CD for AI apps
prompts
agents
search indexes
and infrastructure.,Optimize Azure AI systems for cost
scalability
reliability
quality
and speed.,Design secure reports
notifications
API calls
and automated business actions.,Complete a capstone enterprise GenAI platform with security and governance.

Requirements

No advanced artificial intelligence or machine learning experience is required.,A basic understanding of cloud computing concepts will be helpful
but beginners can follow the course.,Familiarity with Microsoft Azure is useful but not mandatory because the relevant Azure services and architecture responsibilities are explained throughout the course.,Basic programming knowledge in Python
C#
JavaScript
or another modern programming language is recommended for the hands-on activities.,A basic understanding of APIs
databases
web applications
and software architecture will make the technical sections easier to follow.,Students should have access to a computer with a reliable internet connection.,An Azure account or Azure subscription is recommended for students who want to complete the hands-on labs.,Access to Azure OpenAI Service may be required for model deployment and application-building exercises.,A code editor such as Visual Studio Code is recommended.,Familiarity with Git or GitHub is helpful for the DevOps and deployment sections
but it is not required to begin.,The most important requirement is an interest in learning how enterprise Generative AI
RAG
AI agents
security
governance
and Azure cloud services work together.

Description

This course contains the use of artificial intelligence.

Build the skills to design, secure, scale, monitor, and govern modern enterprise Generative AI systems on Microsoft Azure.

This comprehensive course, Enterprise Generative AI Systems on Microsoft Azure, teaches you how to create production-ready Generative AI applications, Retrieval-Augmented Generation systems, enterprise copilots, intelligent assistants, and agentic AI workflows using Microsoft Azure services. Rather than focusing on isolated tools, the course shows you how every component fits together within a complete enterprise architecture.

You will begin by exploring the full Azure Generative AI architecture, including users, applications, enterprise data sources, APIs, compute services, orchestration frameworks, foundation models, retrieval systems, security controls, monitoring, and governance. You will trace a request from the user interface through the application layer, enterprise data, Azure AI Search, Azure OpenAI Service, guardrails, and response delivery.

A major focus of the course is building accurate and reliable RAG applications on Azure. You will learn how to connect structured, semi-structured, and unstructured data from Azure SQL Database, Azure Cosmos DB, Azure Blob Storage, Azure Data Lake, SharePoint, OneDrive, internal APIs, and on-premises systems. You will design ingestion pipelines with Azure Data Factory, process documents using Azure AI Document Intelligence, compare chunking strategies, generate embeddings, and build vector, keyword, hybrid, and semantic search solutions.

The course also covers AI orchestration and autonomous agents. You will explore Microsoft Semantic Kernel, LangChain, and AutoGen while learning how to design planner, executor, and reviewer agents. You will give agents controlled access to enterprise tools, APIs, workflows, memory, and business systems while implementing human approvals, error recovery, timeouts, and protections against uncontrolled agent behavior.

Security and governance are integrated throughout the course. You will learn how to protect AI applications against prompt injection, malicious documents, unsafe content, sensitive-data exposure, unauthorized tool execution, and privilege escalation. You will implement controls using Microsoft Entra ID, role-based access control, managed identities, Azure AI Content Safety, Prompt Shields, Azure Policy, private endpoints, encryption, audit trails, and responsible AI review processes.

You will also learn how to deploy and operate enterprise AI applications using Azure App Service, Azure Container Apps, Azure Functions, Azure DevOps, GitHub Actions, Azure Monitor, and Application Insights. Topics include observability, token usage, latency, retrieval quality, model monitoring, caching, secrets management, cost optimization, scalability, backup, disaster recovery, compliance, and business continuity.

Hands-on labs guide you through designing multichannel assistants, hybrid-search RAG pipelines, secure API gateways, document-processing workflows, AI guardrails, monitoring dashboards, CI/CD pipelines, and cost-management strategies.

By the end of the course, you will complete a capstone project that brings everything together: a secure, scalable, reliable, and governed enterprise GenAI platform on Microsoft Azure. This course is ideal for Azure architects, AI engineers, developers, cloud professionals, security specialists, governance teams, consultants, and technology leaders who want practical expertise in building enterprise-grade AI solutions.

Who this course is for:

Cloud architects who want to design secure and scalable Generative AI solutions on Microsoft Azure.,AI architects and solution architects responsible for enterprise RAG
copilots
AI assistants
and agentic AI systems.,Software developers who want to build production-ready Generative AI applications using Azure OpenAI and related Azure services.,Data engineers who want to build ingestion
document-processing
chunking
embedding
and search pipelines for enterprise AI.,Machine learning engineers who want to move from model experimentation to complete enterprise AI application architecture.,DevOps and platform engineers responsible for deploying
monitoring
scaling
and operating Azure-based AI applications.,Cybersecurity professionals who want to understand prompt injection
AI content safety
identity controls
network security
and secure agent execution.,AI governance
risk
compliance
privacy
and responsible AI professionals who need to understand the technical architecture behind enterprise AI systems.,Technical leads and engineering managers responsible for selecting AI platforms
services
frameworks
and architecture patterns.,Consultants and technology advisors who design Azure AI solutions for enterprise clients.,Product managers and business analysts who work with technical teams to define enterprise AI use cases and delivery requirements.,IT leaders and enterprise architects evaluating how to introduce Generative AI safely across their organizations.,Professionals preparing for roles involving Azure AI architecture
Generative AI engineering
AI platform engineering
or AI solution delivery.,Students and career changers who have basic technical knowledge and want to develop practical skills in Azure Generative AI architecture.,Anyone who wants to understand how Azure OpenAI
Azure AI Search
enterprise data
AI agents
security
governance
monitoring
DevOps
and automation combine into a complete production platform.