Enterprise Generative AI Systems on Microsoft Azure

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.