This article contains an organized list of the best learning resources for .NET developers who are getting started building AI apps. Resources include popular quickstart articles, reference samples, documentation, and training courses.
Resources for Azure OpenAI Service
Azure OpenAI Service provides REST API access to OpenAI's powerful language models. These models can be easily adapted to your specific task including but not limited to content generation, summarization, image understanding, semantic search, and natural language to code translation. Users can access the service through REST APIs, Azure OpenAI SDK for .NET, or the web-based interface in the Azure OpenAI Studio.
The GitHub source version of the Azure OpenAI client library for .NET is an adaptation of OpenAI's REST APIs that provides an idiomatic interface and rich integration with the rest of the Azure SDK ecosystem. It can connect to Azure OpenAI resources or to the non-Azure OpenAI inference endpoint, making it a great choice for even non-Azure OpenAI development.
A collection of 10 samples that demonstrate how to use the Azure OpenAI client library for .NET to chat, stream replies, use your own data, transcribe/translate audio, generate images, etc.
Streaming Chat Completions
A deep link to the samples demonstrating streaming completions.
An article with more complex security scenarios that require Azure role-based access control (Azure RBAC). This document covers how to authenticate to your OpenAI resource using Microsoft Entra ID.
Use the OpenAI dell-e-3 model to generate an image.
Resources for other Azure AI services
In addition to Azure OpenAI Service, there are many other Azure AI services that help developers and organizations rapidly create intelligent, market-ready, and responsbile applications with out-of-the-box and prebuilt customizable APIs and models. Example applications include natural language processing for conversations, search, monitoring, translation, speech, vision, and decision-making.
Azure AI Document Intelligence (formerly Form Recognizer) is a cloud service that uses machine learning to analyze text and structured data from documents. The Document Intelligence software development kit (SDK) is a set of libraries and tools that enable you to easily integrate Document Intelligence models and capabilities into your applications.
The client Library for Text Analytics. This is part of the Azure AI Language service, which provides Natural Language Processing (NLP) features for understanding and analyzing text.
A quickstart article that details how to use Document Translation to translate a source document into a target language while preserving structure and text formatting.
The client library for Conversational Language Understanding (CLU), a cloud-based conversational AI service, which can extract intents and entities in conversations and acts like an orchestrator to select the best candidate to analyze conversations to get best response from apps like Qna, Luis, and Conversation App.
Azure AI Services is a collection of services that are building blocks of AI functionality you can integrate into your applications. In this learning path, you'll learn how to provision, secure, monitor, and deploy Azure AI Services resources and use them to build intelligent solutions.
Training path to help you understand how large language models form the foundation of generative AI: how Azure OpenAI Service provides access to the latest generative AI technology, how prompts and responses can be fine-tuned and how Microsoft's responsible AI principles drive ethical AI advancements.
Azure OpenAI Service provides access to OpenAI's powerful large language models such as ChatGPT, GPT, Codex, and Embeddings models. This learning path teaches developers how to generate code, images, and text using the Azure OpenAI SDK and other Azure services.
AI app templates
AI app templates provide you with well-maintained, easy to deploy reference implementations that provide a high-quality starting point for your AI apps.
There are two categories of AI app templates, building blocks and end-to-end solutions. Building blocks are smaller-scale samples that focus on specific scenarios and tasks. End-to-end solutions are comprehensive reference samples including documention, source code, and deployment to allow you to take and extend for your own purposes.
To review a list of key templates available for each programming language, see AI app templates. To browse all available templates, see the AI app templates on the AI App Template gallery.
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