Explore the diverse and powerful world of core generative AI. This course provides a comprehensive survey of the fundamental models that power modern AI, including Generative Adversarial Networks (GANs), autoregressive models, and diffusion models. You will build a strong foundation, understanding the unique architectures and training strategies for each, and compare essential frameworks like PyTorch and TensorFlow.
The course then moves into hands-on implementation. You will learn to generate sequential data, such as time-series forecasts, using advanced autoregressive models in Azure AI Foundry. Next, you will master the art of high-fidelity image generation, using diffusion models to create and edit stunning visuals with techniques like inpainting and outpainting.
Finally, you will learn to accelerate your development workflow by using Azure ML Designer, a visual, low-code environment for rapid prototyping. You will practice designing, building, evaluating, and preparing sophisticated model pipelines for real-world deployment. This course equips you not just with knowledge of different models, but with the practical skills to build and prototype them effectively on Azure.
This foundational module introduces the diverse landscape of core generative models beyond LLMs. You will explore the distinct architectures and principles behind Generative Adversarial Networks (GANs), autoregressive models, and diffusion models. You will also dive into the practical aspects of model creation by comparing essential training frameworks like PyTorch and TensorFlow and learning the fundamental strategies for training these powerful models on Azure.
Important Notice on the Azure Interface: The screencast videos and screenshots were last updated in late 2025.
Please be aware that Microsoft may have updated the Azure interface since then. If the steps shown in the course materials look different from your current Azure environment, please follow the most up-to-date interface, as the underlying concepts and learning objectives remain the same.
涵盖的内容
6个视频7篇阅读材料6个作业
显示有关单元内容的信息
6个视频•总计27分钟
Introduction to Microsoft Generative AI Engineering certification•4分钟
Introduction to core generative models and techniques course•3分钟
Core models in generative AI•4分钟
Visualizing model outputs: GANs, Autoregressive, and Diffusion•7分钟
Using a pre-trained model in Azure AI Foundry•5分钟
Module 1 summary: From core theories to training fundamentals•3分钟
7篇阅读材料•总计80分钟
Course syllabus and recommended background•5分钟
Exploring GANs, Autoregressive, and Diffusion models•20分钟
Introduction to Model Parameters•10分钟
Insights on model functionality•10分钟
Introduction to training libraries and strategies•15分钟
Analyzing training challenges and strategies•10分钟
Choosing the right model and framework: a case study•10分钟
6个作业•总计210分钟
Module 1 evaluation: Graded Quiz•30分钟
A tour of generative models: First encounters•30分钟
Controlling the output: A parameter tuning activity•30分钟
Core generative models quiz: Practice Quiz•30分钟
Applying model training and evaluation strategies•60分钟
Training strategies assessment: Practice Quiz•30分钟
Implementing autoregressive models
第 2 单元•小时 后完成
单元详情
This module provides a deep dive into autoregressive models, the engines behind sequential data generation. You will focus on their application in tasks like time-series forecasting and text generation. Starting with the basic principles of next-token prediction, you will use Azure AI Foundry to implement models like TimeGEN-1. You will then advance to sophisticated techniques for controlling model output, ensuring your generated sequences are both coherent and high-quality.
Important Notice on the Azure Interface: The screencast videos and screenshots were last updated in late 2025.
Please be aware that Microsoft may have updated the Azure interface since then. If the steps shown in the course materials look different from your current Azure environment, please follow the most up-to-date interface, as the underlying concepts and learning objectives remain the same.
涵盖的内容
5个视频6篇阅读材料5个作业
显示有关单元内容的信息
5个视频•总计16分钟
Mastering sequential data with autoregressive models•3分钟
Sequential data with autoregressive models•4分钟
A first look at generating sequences in Azure AI Foundry•3分钟
Advanced techniques in sequential modeling•3分钟
Module 2 summary: From next-token prediction to advanced forecasting•2分钟
6篇阅读材料•总计65分钟
Autoregressive model techniques•10分钟
Autoregressive models in practice•10分钟
Exploring advanced sequential methods•15分钟
Tradeoffs in advanced sequential modeling techniques•10分钟
From Prototype to Production: Optimizing Sequential Models•10分钟
Case study: building a production-ready forecasting system•10分钟
5个作业•总计210分钟
Module 2 evaluation: Graded Quiz•30分钟
Basic sequential data generation•60分钟
Autoregressive model skills: Practice Quiz•30分钟
Implement autoregressive model techniques for sequential tasks•60分钟
Advanced sequential assessment: Practice Quiz•30分钟
High-fidelity image generation with diffusion models
第 3 单元•小时 后完成
单元详情
This module focuses on the cutting-edge technology of diffusion models for creating and editing stunning, high-fidelity images for any purpose. You will learn the fundamental "denoising" process that allows these models to generate photorealistic visuals—from creative compositions to professional graphics—using simple text prompts. You will then move beyond basic generation to master advanced techniques like inpainting, outpainting, and using negative prompts to gain precise control over your visual outputs. This will equip you to produce tailored, high-quality images for a wide array of business and creative applications.
Important Notice on the Azure Interface: The screencast videos and screenshots were last updated in late 2025.
Please be aware that Microsoft may have updated the Azure interface since then. If the steps shown in the course materials look different from your current Azure environment, please follow the most up-to-date interface, as the underlying concepts and learning objectives remain the same.
涵盖的内容
5个视频5篇阅读材料6个作业
显示有关单元内容的信息
5个视频•总计24分钟
Generating images with diffusion models•3分钟
High-fidelity image generation•6分钟
Introduction to the image generation studio and its controls•7分钟
Mastering image generation with diffusion•7分钟
Module 3 summary: From basic prompts to precise artistic control•2分钟
5篇阅读材料•总计60分钟
Diffusion model fundamentals•15分钟
Diffusion models in image generation•10分钟
Advanced diffusion strategies•15分钟
Analyzing diffusion model outcomes•10分钟
A creative workflow: Analyzing the master image lab•10分钟
6个作业•总计220分钟
Module 3 evaluation: Graded Quiz•30分钟
Generating and refining images with diffusion models•60分钟
Diffusion model skills quiz: Practice Quiz•30分钟
Advanced image editing: Inpainting and Outpainting•30分钟
Combining diffusion techniques for a master image•40分钟
Advanced diffusion skills evaluation: Practice Quiz•30分钟
Foundations of ML pipelines with a visual designer
第 4 单元•小时 后完成
单元详情
In this final module, we pivot from code-centric development to a powerful, high-level approach for accelerating model creation. You will master Azure ML Designer, a visual, drag-and-drop environment for rapid prototyping and pipeline development. You will learn to construct, train, evaluate, and prepare sophisticated models for deployment without writing extensive code. This module equips you with essential MLOps skills, enabling you to build and manage the entire machine learning lifecycle efficiently.
Important Notice on the Azure Interface: The screencast videos and screenshots were last updated in late 2025.
Please be aware that Microsoft may have updated the Azure interface since then. If the steps shown in the course materials look different from your current Azure environment, please follow the most up-to-date interface, as the underlying concepts and learning objectives remain the same.
涵盖的内容
6个视频6篇阅读材料6个作业
显示有关单元内容的信息
6个视频•总计24分钟
From prototype to pipeline with Azure ML Designer•3分钟
Prototyping with Azure ML Designer•4分钟
A guided tour of the Azure ML Designer interface•5分钟
Evaluating advanced prototypes•6分钟
Module 4 summary: From visual prototyping to evaluated pipelines•2分钟
Course summary: Your journey through generative models and techniques•4分钟
6篇阅读材料•总计65分钟
The power of visual prototyping•10分钟
Effective prototyping techniques•10分钟
Anatomy of a designer pipeline•10分钟
From prototyping to deployment•15分钟
Continual improvement in model design•10分钟
Bridging the gap: from visual design to custom code•10分钟
6个作业•总计235分钟
Module 4 evaluation: Graded Quiz•30分钟
Building your first pipeline in Azure ML Designer•45分钟
Creating model prototypes•40分钟
Low code prototyping skills: Practice Quiz•30分钟
From prototype to production: Deploying a designer pipeline•60分钟
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