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- Microsoft Azure offers multiple services that enable developers to build amazing AI-powered solutions. Proper planning and preparation involves identifying the services you'll use and creating an optimal working environment for your development team.
By the end of this module, you'll be able to:
- Identify common AI capabilities that you can implement in applications
- Describe Foundry Tools and considerations for using them
- Describe Microsoft Foundry and considerations for using it
- Identify appropriate developer tools and SDKs for an AI project
- Describe considerations for responsible AI
- Choose the various language models that are available through the Microsoft Foundry's model catalog. Understand how to select, deploy, and test a model, and to improve its performance.
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.
- Use the Microsoft Foundry SDK to develop AI applications with Microsoft Foundry projects.
After completing this module, you'll be able to:
- Describe capabilities of the Microsoft Foundry SDK.
- Use the Microsoft Foundry SDK to work with connections in projects.
- Use the Microsoft Foundry SDK to develop an AI chat app.
- Get started with prompt flow to develop language model apps in the Microsoft Foundry.
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.
- Retrieval Augmented Generation (RAG) is a common pattern used in generative AI solutions to *ground* prompts with your data. Microsoft Foundry provides support for adding data, creating indexes, and integrating them with generative AI models to help you build RAG-based solutions.
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 an agent using RAG on your own data in the Microsoft Foundry portal
- When you want to maximize the consistency in the responses of your language model, you can fine-tune a base model before integrating the model into your chat application. Learn how to fine-tune a language model and then integrate the model with Microsoft Foundry.
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 Microsoft Foundry portal.
- A practical approach to implementing generative AI responsibly, and exploring content filters in AI Foundry portal.
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
- Evaluating copilots is essential to ensure your generative AI applications meet user needs, provide accurate responses, and continuously improve over time. Discover how to assess and optimize the performance of your generative AI applications using the tools and features available in the Azure AI Studio.
By the end of this module, you'll be able to:
- Understand model benchmarks.
- Perform manual evaluations.
- Assess your generative AI apps with AI-assisted metrics.
- Configure evaluation flows in the Microsoft Foundry portal.
