Understand Microsoft Foundry capabilities
Microsoft Foundry portal provides a user interface based around hubs and projects. In general, creating a hub provides more comprehensive access to Azure AI and Azure Machine Learning. Within a hub, you can create projects. Projects provide more specific access to models and agent development. You can manage your projects from Microsoft Foundry portal's overview page.
When you create an Azure AI Hub, several other resources are created in tandem, including a Foundry Tools resource. In Microsoft Foundry portal, you can test all kinds of Foundry Tools, including Azure Speech, Azure Language, Azure Vision, and Microsoft Foundry Content Safety.
In addition to demos, Microsoft Foundry portal provides playgrounds to test Foundry Tools and other models from the model catalog.
Customizing models
There are many ways to customize the models in generative AI applications. The purpose of customizing your model is to improve aspects of its performance, including quality and safety of the responses. Let's take a look at four of the main ways you can customize models in Microsoft Foundry.
| Method | Description |
|---|---|
| Using grounding data | Grounding refers to the process of ensuring that a system's outputs are aligned with factual, contextual, or reliable data sources. Grounding can be done in various ways, such as linking the model to a database, using search engines to retrieve real-time information, or incorporating ___domain-specific knowledge bases. The goal is to anchor the model's responses to these data sources, enhancing the trustworthiness and applicability of the generated content. |
| Implementing Retrieval-Augmented Generation (RAG) | RAG augments a language model by connecting it to an organization's proprietary database. This technique involves retrieving relevant information from a curated dataset and using it to generate contextually accurate responses. RAG enhances the model's performance by providing it with up-to-date and ___domain-specific information, which helps in generating more accurate and relevant answers. RAG is useful for applications where real-time access to dynamic data is crucial, such as customer support or knowledge management systems. |
| Fine-tuning | Involves taking a pretrained model and further training it on a smaller, task-specific dataset to make it more suitable for a particular application. This process allows the model to specialize and perform better at specific tasks that require ___domain-specific knowledge. Fine-tuning is useful for adapting models to ___domain-specific requirements, improving accuracy, and reducing the likelihood of generating irrelevant or inaccurate responses. |
| Managing security and governance controls | Security and governance controls are needed to manage access, authentication, and data usage. These controls help prevent the publication of incorrect or unauthorized information. |
Next, let's understand how Microsoft Foundry provides tools for generative AI application performance evaluation.