Build Powerful AI Solutions with Azure AI Studio

Table of Contents

1. Introduction
2. The Global AI Notes event
3. Introducing the Speakers
4. About Azure AI Studio
5. Goals for Today's Session
6. Building a Custom Co-pilot: Getting Started
7. Setting Up the Development Environment
8. Building a Search Index
9. Developing the Custom Co-pilot
10. Evaluating the Co-pilot's Performance
11. Deploying the Co-pilot to an Endpoint
12. testing and Comparing Evaluations
13. Future Capabilities and Integration Options
14. Conclusion

Building a Custom Co-pilot with Azure AI Studio

In today's session of the Global AI Notes event, we will explore how to build a custom co-pilot using Azure AI Studio. We have an exciting lineup of speakers ready to share their insights and experiences, so stay tuned!

Introduction

Hello and welcome! Today, we will dive into the world of Azure AI Studio and learn how to create a custom co-pilot. Whether you are new to AI development or an experienced developer, this session will equip you with the knowledge and tools to build your own co-pilot using Python and the AI SDK and CLI. Let's get started!

The Global AI Notes Event

The Global AI Notes event is a platform for AI enthusiasts and experts from around the world to come together and share their knowledge. Today's session focuses on Azure AI Studio and how it can be used to build custom co-pilots. We have three speakers lined up: Leia, Rob, and Hani. Let's hear from each of them about their background and what they will be discussing today.

Leia - Product Manager at Azure AI Experiences Team

Leia is a product manager at the Azure AI Experiences team. With her background in development and transition to product management, she brings a unique perspective to the field. Leia will be sharing some exciting developments in Azure AI Studio and the co-pilot approach. Get ready for some fascinating insights!

Rob - Architect and Engineering Manager at AI Platform Division

As an architect and engineering manager at the AI Platform Division, Rob has been at Microsoft for a long time. He is passionate about bringing AI technology to customers and partner teams to create amazing experiences. Today, Rob will be discussing the technical aspects of building a custom co-pilot and sharing his expertise.

Hani - Azure AI SDK Developer

Hani has been with Microsoft for close to seven years and is an expert in the Azure AI SDK. He has been working on the Azure AI Studio Development Kit (ASDK) and is excited to share his knowledge with you today. Hani will be demonstrating how to utilize the AI SDK and CLI to build a powerful co-pilot. Get ready for some hands-on learning!

About Azure AI Studio

Azure AI Studio is a powerful development environment that provides tools and resources for building AI solutions. It offers a seamless integration with the AI SDK and CLI, allowing developers to code, test, and deploy their solutions with ease. Whether you are an AI beginner or an experienced developer, Azure AI Studio has everything you need to bring your AI projects to life.

Goals for Today's Session

Our goal for today's session is to equip you with the skills and knowledge to build your own custom co-pilot using Azure AI Studio. We will guide you through the step-by-step process, from setting up the development environment to deploying your co-pilot to an endpoint. By the end of this session, you will be ready to create enterprise-grade co-pilots grounded in your own custom data.

Building a Custom Co-pilot: Getting Started

To build a custom co-pilot, we will be using the retrieval augmented generation (RAG) approach. This approach allows us to create co-pilots that are grounded in custom data, providing accurate and contextually Relevant responses to user queries. Our co-pilot today will be designed for a fictitious company called "Treeholics", who need a chatbot for their website to assist customers with tree-related products and services.

Step 1: Setting Up the Development Environment

Before we can start building our custom co-pilot, we need to set up our development environment. We have two main options: working locally or working in the cloud. If you choose to work locally, we will guide you through the installation process of the AI SDK and CLI. However, for today's session, we will be using the Azure AI Studio curated development environment for VS Code, which comes pre-configured with the necessary tools and capabilities.

Step 2: Building a Search Index

To provide relevant responses, our co-pilot needs access to relevant documents. We will build a search index using the Azure AI Search command from the CLI. This index will be based on the custom data provided by Treeholics, which includes product data and customer sales data. The search index will allow our co-pilot to retrieve relevant documents when responding to user queries.

Step 3: Developing the Custom Co-pilot

With our development environment set up and the search index in place, we can now start developing the custom co-pilot. We will be using a sample co-pilot code as the foundation and adapting it for our use case. This code includes a chat completion function that takes user messages, embeds the question, performs a vector search on the search index, and generates a response using the Azure GPT model. We will customize this code to meet the specific requirements of Treeholics.

Step 4: Evaluating the Co-pilot's Performance

Once we have developed our custom co-pilot, it's important to evaluate its performance. We will use AI-assisted metrics to assess the quality of the responses and identify areas for improvement. By comparing the co-pilot's performance against an evaluation dataset, we can measure its groundedness, relevance, coherence, and similarity scores. This evaluation process will help us fine-tune our co-pilot to provide the best possible user experience.

Step 5: Deploying the Co-pilot to an Endpoint

With our co-pilot developed and evaluated, it's time to deploy it to an endpoint. This will allow us to share our co-pilot with others and integrate it into external applications. Using the deploy command from the CLI, we can Package our code, securely handle secrets, and deploy the co-pilot to a managed endpoint in Azure. This deployment process ensures that our co-pilot runs consistently in a production environment.

Testing and Comparing Evaluations

Once our co-pilot is deployed, we can test it out using the interactive chat capabilities. By providing sample questions and observing the co-pilot's responses, we can fine-tune and improve its performance. We can also compare different evaluations to see how changes to the code and Prompt affect the co-pilot's metrics. This iterative process allows us to continuously optimize our co-pilot based on user feedback and real-world usage.

Future Capabilities and Integration Options

While today's session focused on the essentials of building a custom co-pilot, there are many additional capabilities and integration options available with Azure AI Studio. The AI CLI and SDK support various programming languages and offer code templates for generating webpage interfaces. There are also plans for future features, including custom metrics for evaluation and multi-language support. Azure AI Studio is continuously evolving to meet the needs of developers and provide a seamless AI development experience.

Conclusion

Building a custom co-pilot using Azure AI Studio allows developers to create powerful, contextually aware AI solutions. By following the step-by-step process outlined in this session, you can develop your own co-pilot grounded in custom data and deploy it to an endpoint. We hope that this session has equipped you with the knowledge and tools needed to embark on your own AI development journey. Thank you for joining us, and happy coding!

Highlights

• Azure AI Studio provides a powerful development environment for building AI solutions
• Building a custom co-pilot involves setting up the development environment, building a search index, developing the co-pilot code, evaluating its performance, and deploying it to an endpoint
• Evaluating the co-pilot's performance involves using AI-assisted metrics and comparing the results against an evaluation dataset
• Future capabilities of Azure AI Studio include custom metrics for evaluation and multi-language support

Resources:

• Azure AI Studio
• Azure AI SDK
• Azure AI CLI

FAQ

1. Q: Can I develop a co-pilot using a local development environment?

   • A: Yes, you can choose to work locally by installing the Azure AI SDK and CLI in your development environment. This allows you to have more control over your development process.

2. Q: Is it possible to customize the metrics used for evaluating the co-pilot's performance?

   • A: Currently, the Azure AI Studio supports predefined metrics for evaluation. However, there are plans to introduce custom metrics in future updates, allowing developers to define their own evaluation criteria.

3. Q: Can I integrate the co-pilot with other Microsoft services or external applications?

   • A: Absolutely! The co-pilot can be integrated with other Microsoft services, such as Bot Framework, or any external application that can make use of API endpoints. Azure AI Studio provides the flexibility to connect and collaborate with various services and platforms.

4. Q: Are there any limitations in terms of programming languages supported by the Azure AI SDK and CLI?

   • A: No, the Azure AI SDK supports multiple popular programming languages, including Python, JavaScript, and TypeScript. You can choose the language that best suits your development preferences.

5. Q: Is it possible to deploy the co-pilot to multiple endpoints for scalability?

   • A: Yes, the Azure AI Studio allows you to deploy the co-pilot to multiple managed endpoints, providing scalability and ensuring your co-pilot can handle increased user demand.

6. Q: Can I leverage pre-trained models and datasets for my custom co-pilot?

   • A: Yes, Azure AI Studio provides access to pre-trained models and datasets that can be used as a foundation for your custom co-pilot. These models and datasets can be customized and adapted to meet your specific requirements.

7. Q: Can I Visualize the evaluation results in a more user-friendly format?

   • A: The evaluation results can be viewed in a tabular format on the command line or as generated output files. However, if you prefer a more visual representation, you can use visualization tools such as Pandas or Matplotlib to load and analyze the JSON output files.