Azure OpenAI Service provides access to OpenAI’s powerful large language models such as GPT; the model behind the popular ChatGPT service. These models enable various natural language processing (NLP) solutions to understand, converse, and generate content. Users can access the service through REST APIs, SDKs, and Azure OpenAI Studio.
In this course, you’ll learn how to provision Azure OpenAI service, deploy models, and use them in generative AI applications. Note: Generative AI is a fast-evolving field of artificial intelligence, and the Azure OpenAI service is subject to frequent changes. The course materials are maintained to reflect the latest version of the service at the time of writing.
Objetivos
- Deploy an Azure OpenAI resource and an Azure OpenAI model
- Generate natural language responses by using Azure OpenAI
- Apply prompt engineering techniques by using Azure OpenAI
- Generate and improve code by using Azure OpenAI
- Generate images with DALL-E in Azure OpenAI
- Use Azure OpenAI on your data
Destinatários
The audience for this course includes software developers and data scientists who need to use large language models for generative AI. Some programming experience is recommended, but the course will be valuable to anyone seeking to understand how the Azure OpenAI service can be used to implement generative AI solutions.
Pré-Requisitos
Before starting this learning path, you should already have:
- Familiarity with Azure and the Azure portal.
- Experience programming with C# or Python.
Programa
- Get started with Azure OpenAI Service
- Build natural language solutions with Azure OpenAI Service
- Apply prompt engineering with Azure OpenAI Service
- Generate code with Azure OpenAI Service
- Generate images with Azure OpenAI Service
- Implement Retrieval Augmented Generation (RAG) with Azure OpenAI Service
- Fundamentals of Responsible Generative AI
Get started with Azure OpenAI Service
This module provides engineers with the skills to begin building an Azure OpenAI Service solution.
- Access Azure OpenAI Service
- Use Azure OpenAI Studio
- Explore types of generative AI models
- Deploy generative AI models
- Use prompts to get completions from models
- Test models in Azure OpenAI Studio’s playgrounds
- Exercise – Get started with Azure OpenAI Service
Build natural language solutions with Azure OpenAI Service
This module provides engineers with the skills to begin building apps that integrate with the Azure OpenAI Service.
- Integrate Azure OpenAI into your app
- Use Azure OpenAI REST API
- Use Azure OpenAI SDK
- Exercise – Integrate Azure OpenAI into your app
Apply prompt engineering with Azure OpenAI Service
Prompt engineering in Azure OpenAI is a technique that involves designing prompts for natural language processing models. This process improves accuracy and relevancy in responses, optimizing the performance of the model.
- Understand prompt engineering
- Write more effective prompts
- Provide context to improve accuracy
- Exercise – Utilize prompt engineering in your application
Generate code with Azure OpenAI Service
This module shows engineers how to use the Azure OpenAI Service to generate and improve code.
- Construct code from natural language
- Complete code and assist the development process
- Fix bugs and improve your code
- Exercise – Generate and improve code with Azure OpenAI Service
Generate images with Azure OpenAI Service
The Azure OpenAI service includes the DALL-E model, which you can use to generate original images based on natural language prompts.
- What is DALL-E?
- Explore DALL-E in Azure OpenAI Studio
- Use the Azure OpenAI REST API to consume DALL-E models
- Exercise – Generate images with a DALL-E model
Implement Retrieval Augmented Generation (RAG) with Azure OpenAI Service
Azure OpenAI on your data allows developers to implement RAG with supported AI chat models to reference specific sources of data to ground the response.
- Understand Retrieval Augmented Generation (RAG) with Azure OpenAI Service
- Add your own data source
- Chat with your model using your own data
- Exercise – Add your data for RAG with Azure OpenAI Service
Fundamentals of Responsible Generative AI
Generative AI enables amazing creative solutions, but must be implemented responsibly to minimize the risk of harmful content generation.
- Plan a responsible generative AI solution
- Identify potential harms
- Measure potential harms
- Mitigate potential harms
- Operate a responsible generative AI solution
- Exercise – Explore content filters in Azure OpenAI
