Enterprise Generative AI Systems on Microsoft Azure
Published 7/2026
Created by School of AI, Arjun Vaid
MP4 | Video: h264, 3840x2160 | Audio: AAC, 44.1 KHz, 2 Ch Level: All Levels | Genre: eLearning | Language: English | Duration: 351 Lectures ( 44h 3m ) | Size: 50.3 GB
Design secure, scalable, reliable, and governed RAG and agentic AI architectures using Azure services
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.
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