Duration
1 day
Course Overview
Generative Artificial Intelligence (AI) is becoming more accessible through easy-to-use platforms like Azure AI Studio. Learn how to build generative AI applications like custom copilots that use language models and prompt flow to provide value to your users.
Prerequisites
Before starting this module, you should be familiar with fundamental AI concepts and services in Azure. Consider completing the Get started with artificial intelligence learning path first.
Module 1: Introduction to Azure AI Studio
The growth in the use of artificial intelligence (AI) in general, and generative AI in particular means that developers are increasingly required to create comprehensive AI solutions. These solutions need to combine machine learning models, AI services, prompt engineering solutions, and custom code. Microsoft Azure provides multiple services that you can use to create AI solutions, but until now, developers have needed to work with multiple tools and web portals in a single project.
Azure AI Studio brings together capabilities from Azure Machine Learning, Azure OpenAI service, and other Azure AI services to provide a single, centralized workspace within which developers can collaborate with data scientists and others to build AI solutions.
In this module, you'll explore Azure AI Studio and learn how you can use it to create and manage AI development projects.
Module 2: Explore and deploy models from the model catalog in Azure AI Studio
Explore the various language models that are available through the Azure AI Studio's model catalog. Understand how to select, deploy, and test a model, and to improve its performance.
Learning objectives
By the end of this module, you'll be able to:
- Select a language model from the model catalog.
- Deploy a model to an endpoint.
- Test a model and improve the performance of the model.
Module 3: Get started with prompt flow to develop language model apps in the Azure AI Studio
Learn about how to use prompt flow to develop applications that leverage language models in the Azure AI Studio.
Learning objectives
By the end of this module, you'll be able to:
- Understand the development lifecycle when creating language model applications.
- Understand what a flow is in prompt flow.
- Explore the core components when working with prompt flow.
Module 4: Build a RAG-based copilot solution with your own data using Azure AI Studio
Copilots can work alongside you to provide suggestions, generate content, or help you make decisions. Copilots use language models as a form of generative artificial intelligence (AI) and will answer your questions using the data they were trained on. To ensure a copilot retrieves information from a specific source, you can add your own data when building a copilot with the Azure AI Studio.
Learning objectives
By the end of this module, you'll be able to:
- Identify the need to ground your language model with Retrieval Augmented Generation (RAG)
- Index your data with Azure AI Search to make it searchable for language models
- Build a copilot using RAG on your own data in the Azure AI Studio
Module 5: Integrate a fine-tuned language model with your copilot in the Azure AI Studio
Train a base language model on a chat-completion task. The model catalog in the Azure AI Studio offers many open-source models that can be fine-tuned for your specific model behavior needs.
Learning objectives
By the end of this module, you'll be able to:
- Understand when to fine-tune a model.
- Prepare your data to fine-tune a chat completion model.
- Fine-tune a base model in the Azure AI Studio.
Module 6: Evaluate the performance of your custom copilot in the Azure AI Studio
Evaluating copilots is essential to ensure your custom copilots meet user needs, provide accurate responses, and continuously improve over time. Discover how to assess and optimize the performance of your custom copilot using the tools and features available in the Azure AI Studio.
Learning objectives
By the end of this module, you'll be able to:
- Understand model benchmarks.
- Perform manual evaluations.
- Assess your copilot with AI-assisted metrics.
- Configure evaluation flows in the Azure AI Studio.
Module 7: Responsible generative AI in AI Studio
Generative AI enables amazing creative solutions, but must be implemented responsibly to minimize the risk of harmful content generation.
Learning objectives
By the end of this module, you'll be able to:
- Describe an overall process for responsible generative AI solution development
- Identify and prioritize potential harms relevant to a generative AI solution
- Measure the presence of harms in a generative AI solution
- Mitigate harms in a generative AI solution
- Prepare to deploy and operate a generative AI solution responsibly
Programme scope, delivery arrangements and suitability are confirmed during enquiry.