> ## Documentation Index
> Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Module Introduction
> Introduction to provisioning Azure OpenAI resources, deploying generative models, and using the Azure AI Foundry portal for experimentation, model management, and governance
Getting Started with Azure OpenAI Service
[Azure OpenAI](https://learn.microsoft.com/azure/cognitive-services/openai/) pairs [OpenAI](https://openai.com/)'s advanced large language models (for example, [GPT](https://openai.com/research/gpt-4)) with Azure’s enterprise-grade security, governance, and compliance. This module introduces how to provision Azure OpenAI resources, deploy generative models, and use the Azure AI Foundry portal to run experiments and manage assets.
Why this matters: organizations use Azure OpenAI to accelerate development of conversational agents, summarization pipelines, and other generative AI solutions while maintaining control over data residency, access, and auditability.
Learning objectives
| Objective | What you’ll learn | Where it applies |
| --------------------------- | ---------------------------------------------------------------- | ---------------------------------------------------------------------------- |
| Understand generative AI | Distinguish generative AI from traditional ML and when to use it | Evaluating use cases such as chatbots, content generation, and summarization |
| Deploy and configure models | Create Azure OpenAI resources, pick models, and set up endpoints | Production and development deployments, SDK and REST usage |
| Use Azure AI Foundry portal | Run experiments, catalog models, and manage AI assets | Experimentation, governance, and model lifecycle management |
What to expect in this module
* A concise overview of generative AI concepts and how they differ from predictive or classification models.
* Step-by-step guidance for provisioning an Azure OpenAI resource, selecting a model (e.g., GPT family), and creating an API endpoint.
* Introduction to the Azure AI Foundry portal for experimentation, model versioning, and asset management.
Quick prerequisites
* An active Azure subscription and permission to create Cognitive Services/OpenAI resources.
* Basic familiarity with REST APIs or one of the Azure SDKs for your preferred language.
* Understanding of common prompts, token usage, and cost implications for large models.
Before you begin: access to Azure OpenAI may require requesting access or enabling preview features depending on your subscription and region. Check the Azure OpenAI quickstart and subscription requirements before provisioning resources.
Next resources
* Azure OpenAI documentation and quickstarts: [https://learn.microsoft.com/azure/cognitive-services/openai/quickstart?tabs=command-line](https://learn.microsoft.com/azure/cognitive-services/openai/quickstart?tabs=command-line)
* Azure AI overview and services: [https://learn.microsoft.com/azure/ai-services/](https://learn.microsoft.com/azure/ai-services/)
* OpenAI research (GPT family): [https://openai.com/research/gpt-4](https://openai.com/research/gpt-4)
By the end of this module you’ll be equipped to create an Azure OpenAI resource, deploy a model endpoint, and begin iterating on experiments using the Azure AI Foundry portal.