Getting started with generative AI in Azure

Microsoft via Coursera

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

4 weeks, 3 hours a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English, Arabic, Pashto, Korean, Portuguese, Japanese, Italian, Indonesian, French, Farsi, Spanish

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Overview

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Dive into the transformative world of generative AI with Microsoft Azure! This course is your hands-on introduction to building intelligent applications, taking you from core concepts to completing multiple hands-on projects. You will start by understanding the fundamentals of what makes generative AI unique and explore its evolution from traditional AI models to the generative approach. Next, you'll get acquainted with the Microsoft Foundry, Microsoft's powerful platform for AI development. Through practical labs and activities, you will learn to set up your environment, experiment with different AI models, and configure key parameters. The course culminates in you designing, building, and refining your own text generation application. Crucially, you will also learn to integrate essential ethical principles, ensuring your AI solutions are responsible and safe. By the end, you won't just understand generative AI; you'll have the practical skills to build with it.

Syllabus

Fundamentals of Generative AI
- Generative AI is reshaping how software is built and how decisions are made — and this module gives you the conceptual foundation you need before touching any tools. You start by examining why generative AI matters and what real-world problems it is being applied to, grounding the technology in practical significance rather than hype. From there, you build a precise mental model of the AI landscape, distinguishing generative AI from discriminative AI and situating both within the broader fields of Machine Learning and Deep Learning. A hands-on screencast then shows these distinctions in action on the Microsoft Foundry platform, so you see the concepts working rather than just reading about them. The module closes by tracing the architectural milestones — from early neural networks through backpropagation and the transformer era — that explain why today's generative models behave as they do. By the end, you will be able to explain what generative AI is, differentiate it from related approaches, and describe the key developments that led to it, giving you a reliable conceptual anchor for everything that follows.
Getting Acquainted with Microsoft Foundry
- With the conceptual groundwork in place, you now move into the platform where your AI work will happen. This module introduces Microsoft Foundry — the current name for what was previously Azure AI Foundry — and takes you through setting up your own environment, navigating its key interfaces, and configuring AI models for practical use. The core of the module is experimentation. You learn what AI experimentation means and why a structured approach to it matters, then run your own experiments: adjusting generation parameters such as temperature, top-p, and reasoning effort, and observing directly how each setting shapes model output. The hands-on labs are scaffolded so that each step builds on the last. By the end, you will be confident navigating Foundry, able to set up and run experiments independently, and equipped to interpret results — a foundation you will build on directly when you begin developing your first AI application.
Building Your First AI application
- This is where you build. The module takes you from design to a working text generation application, with each lesson scaffolded so that you understand the choices you are making before you make them. The first lesson focuses on the blueprint: you examine what makes a well-designed text generation application, choose an appropriate model for your use case, and understand how the API layer will connect your backend to the Foundry endpoint. You then get hands-on in a re-recorded screencast on the current Microsoft Foundry portal, and build your application's core framework in a structured practice activity — with a pre-built UI provided so your attention stays on the AI integration. The second lesson shifts to testing and refinement. You learn how to evaluate your application's outputs, apply prompt engineering techniques to improve them, and use the practice quiz and graded assessment to confirm your understanding before moving ahead. Note: this module deliberately does not cover fine-tuning or MLOps — those are intermediate-to-advanced topics outside the course scope. The focus is on building a functional, well-tested application using pre-built models.
Ethical AI Usage and Project Completion
- Building a capable AI application is only part of the job — building one you can stand behind is the other. This final module weaves responsible AI directly into your development practice rather than treating it as an afterthought. First Lesson gives you the ethical grounding: what the core principles of responsible AI are, how bias and hallucinations arise and why they matter, and how to create a practical evaluation framework. The lesson sequence has been reordered so that the ethical framework comes before the bias-and-hallucinations deep dive, giving you the principles before the problems. A re-recorded screencast on the current Microsoft Foundry portal then shows you Azure's built-in responsible AI toolkit in action. Second Lesson moves into application: you implement content filters and prompt injection defences in a hands-on activity, work through a coach dialogue that connects principle to practice, and complete the final project — an enhanced, documented text generation application that integrates both technical functionality and the ethical safeguards you have just learned. A role play and graded assessment close the module, giving you a chance to demonstrate readiness before the course summary.