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.

Core generative models and techniques

Core generative models and techniques
This course is part of Microsoft Generative AI Engineering Professional Certificate

Instructor: Microsoft
Access provided by PALC Dev
Recommended experience
Skills you'll gain
- Data Pipelines
- Image Quality
- Model Deployment
- Forecasting
- Image Analysis
- Generative Model Architectures
- Time Series Analysis and Forecasting
- MLOps (Machine Learning Operations)
- Model Evaluation
- Generative Adversarial Networks (GANs)
- Generative AI
- Microsoft Azure
- Tensorflow
- Prototyping
- Deep Learning
- PyTorch (Machine Learning Library)
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23 assignments
January 2026
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There are 4 modules in this course
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.
What's included
6 videos7 readings6 assignments
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.
What's included
5 videos6 readings5 assignments
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.
What's included
5 videos5 readings6 assignments
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.
What's included
6 videos6 readings6 assignments
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