
Azure AI Foundry
Generative AI Ecosystem
What to expect from the course
Trivia time to have light-hearted moments and learn a few things.
The Beginning of a Future
What is Azure AI foundry and Why do we need it
Azure AI Foundry Components and Features
Navigating the Azure AI Foundry Portal
Cost Considerations of a Project in Azure AI Foundry
Learn about AI Models in the Model Catalog
Walkthrough of a specific AI Model (DeepSeek R1) within Azure AI Foundry
Segregation of all AI models in Azure AI foundry
Innovation in Life Sciences with help from models in Azure AI foundry
Your Dedication is Shining
Azure AI Foundry Playground
Playground in Azure AI Foundry - Testing Dall-E model to create images from texts.
You are a Challenger
Overview of Retrieval Augmented Generation (RAG)
Steps for Developing a Retrieval Augmented Generation (RAG) Solution
Demonstration of a RAG Solution in Azure AI Foundry.
Overview of the RAG Mini Project
In this hands-on project, you'll build an intelligent document Q&A system that can answer questions based on uploaded documents. You'll learn how Azure AI Foundry enables you to create AI applications without extensive coding.
A few scenarios for your consideration and thought.
Introduction to Responsible AI
Responsible AI Framework in Azure AI Foundry
Implement Safety and Security in Azure AI Foundry
Excellence is Your Habit
Azure AI Foundry Ecosystem - hub, project and management centre
Create a Project in Azure AI Foundry
Build and Customise AI solutions in Azure AI Foundry
Deploying, accessing and testing an OpenAI model in Azure AI foundry
Safety and Security in Azure AI Foundry for AI solutions
Trace your AI application to optimize it.
Monitor your AI solution to optimise for better performance.
Management Center in Azure AI Foundry
Project Assignment - Problem Statement : Build an Agentic AI solution.
Lets discuss Agentic AI with examples from Energy and Manufacturing sector
Agentic AI - Components, Process flow and Execution steps
Design principles for the solution.
Create an Agent for predicting energy demand by providing required instruction to the agent, grounding its response on given custom knowledge and testing in the playground.
A few best practices for agent creation, configuration and management.
Let's create a communication agent and plan the hand of between the agents.
Let's understand a few best practices for hand off and why it should be handled well.
Learn the approach to integrate with enterprise systems
What are Azure AI Services?
Azure AI Services - Speech
Azure Text to Speech Demonstration
Azure AI Services - Language Translation
Azure AI Services - Vision and Document Service (Content Understanding)
You are a Champion. Keep Going.
Unlock the power of Artificial Intelligence in the cloud with this comprehensive, project-based tutorial on AI Development with Microsoft Azure AI Foundry. Whether you're an aspiring AI engineer, cloud developer, or a professional looking to upskill, this course will guide you through building, deploying, and managing real-world AI solutions end-to-end.
In this hands-on learning experience, you’ll explore the full Azure AI ecosystem and learn how to transform ideas into production-ready AI applications.
What You Will Learn:
• End-to-End AI Solution Development — From ideation to deployment, monitoring, and lifecycle management.
• Navigating Azure AI Foundry — Master the platform’s interface, tools, and capabilities including Hubs, Projects, and the Management Centre.
• RAG (Retrieval-Augmented Generation) — Build intelligent solutions that blend LLMs with enterprise data for improved accuracy.
• Azure AI Agents — Learn configuration, deployment, handoff workflows, and agent management best practices.
• Model Deployment & Endpoint Integration — Configure and deploy AI models, access them via endpoints, and test them in the Azure Playground.
• Monitoring, Tracing & Observability — Track model performance, diagnose issues, and ensure operational excellence post-deployment.
• Azure AI Services — Deep dive into Speech, Text-to-Speech, Vision, Document Intelligence, and Translation services.
• Safety, Security & Responsible AI — Apply responsible AI principles and implement guardrails to build secure, ethical AI applications.
• AI Model Segregation — Organize models by industry, capability, license, and provider for optimized solution design.
• Cost Planning & Optimization — Understand pricing, consumption, and cost considerations for scalable AI solutions.
• AI Solution Lifecycle Management — Gain clarity on building sustainable, maintainable AI systems from start to finish.
Why This Course Stands Out:
Project-based learning that ensures real-world readiness
Clear explanations for both beginners and experienced professionals
Focus on production-grade AI development, not just experimentation
Full coverage of the Azure AI Foundry ecosystem and modern AI practices
Practical insights on deploying, monitoring, and scaling AI solutions
By the end of this tutorial, you’ll be fully equipped to design robust AI architectures, deploy intelligent models, integrate Azure AI services, and manage complete solutions with confidence.
If you’re ready to build powerful AI applications in the cloud—this course is your roadmap. Enroll now and start your journey into Azure-powered AI innovation!
Disclaimer:
This course is for educational purposes only. Any practical implementation of AI models and services should be conducted with due diligence and in compliance with ethical and regulatory guidelines. This tutorial does not serve as a real-time implementation guide.