How to use Microsoft Azure AI Studio and Azure OpenAI models
TLDRThis video tutorial dives into the capabilities of Microsoft Azure AI Studio, a tool designed for both novice and professional developers aiming to create advanced AI applications. The tutorial covers the essentials of Azure AI Studio, including building and deploying models with Azure OpenAI, managing and importing data, and integrating multiple AI services. It also explores using the Azure API for projects, enhancing AI models with custom data, and the seamless integration of different programming environments. The video is sponsored by Microsoft, highlighting its commitment to facilitating powerful AI solutions.
Takeaways
- 🌟 Azure AI Studio is a comprehensive platform for building complex AI solutions, suitable for both beginners and professional developers.
- 📚 The platform integrates various AI services, including Azure OpenAI, machine learning, speech, and vision services, in a centralized location.
- 💡 Users can deploy and test models on Azure OpenAI, add custom data, define prompts, and integrate content safety filters within Azure AI Studio.
- 🔍 Azure AI Studio allows for the combination of multiple AI capabilities to create advanced generative AI solutions.
- 🚀 The platform provides a visual interface, similar to an AI playground, with features like the prompt flow for creating complex interactions.
- 📈 Custom data sources can be added to the model from various formats like CSV, databases, or documents to enhance the model's performance.
- 📝 Data sources can be managed within the platform, and users can upload files for the model to reference, improving the accuracy of responses.
- 🔗 The API functionality is explored, showing how to access and use the model for projects, including how to authenticate and make requests.
- 🔧 Users can configure and deploy models, with options to select different models and manage deployments from the dashboard.
- 📱 Azure AI Studio supports the creation of web apps and provides code snippets in various programming languages for easy integration.
- ✅ The video includes a step-by-step guide on how to set up a project, install necessary packages, and use the Azure AI Studio API in a coding environment like VS Code.
Q & A
What is Azure AI Studio and what does it combine?
-Azure AI Studio is a platform that combines tools from Microsoft such as Azure Open AI, machine learning, and other AI services like speech and vision, all in a central place. It allows users to deploy models, test them on Azure Open AI service, add custom data for better prompting, define prompts, integrate content safety filters, and combine multiple AI capabilities.
How can one start using Azure AI Studio?
-To start using Azure AI Studio, one needs to sign in with a user account. After signing in, users can create a new project, select a model to use, and deploy it to start utilizing the studio's features.
What is the purpose of the system message in Azure AI Studio?
-The system message in Azure AI Studio initializes the context for the AI and its chat session. It sets the stage for the interaction and can be customized according to the user's needs.
How can variables be used in Azure AI Studio?
-Variables in Azure AI Studio can be used to store information that can be referenced within system prompts and chat prompts. This is useful for building applications where specific information, such as programming languages, can be incorporated into the AI's responses.
What is the role of the 'prompt flow' feature in Azure AI Studio?
-The 'prompt flow' feature in Azure AI Studio allows users to visually showcase and manage what's happening in a more complex prompt. It helps create a flowchart-like structure for the interaction, enabling customization of the models and how they function.
How can custom data be added to a model in Azure AI Studio?
-Custom data can be added to a model in Azure AI Studio by selecting 'Add your data' and then 'Add a data source'. Users can choose from Azure AI search, Azure blob storage, or upload a file. After selecting the data source and providing necessary details like Azure subscription and resource, users can upload the file and have it indexed for use in the model.
What are the steps to deploy a model on Azure AI Studio?
-To deploy a model on Azure AI Studio, one needs to select the 'Deployments' tab, choose to create a new deployment, select the model, and confirm the creation. If access to a specific model like GPT 4 requires approval, a request must be submitted to the Azure Open AI service team. Once approved, the model can be deployed and used immediately in the playground or with the API.
How can the API be used for a project in Azure AI Studio?
-The API can be used for a project by first obtaining the necessary keys and endpoint URL from the Azure AI Studio dashboard. Then, in the coding environment, the required packages are installed, and the environmental keys are set up. A prompt can be defined, and an asynchronous function can be created to call the API, passing in the model, endpoint, and prompt to get the desired response.
What are the different types of deployments available in Azure AI Studio?
-Azure AI Studio allows for different types of deployments, including web apps and real-time endpoints. Users can select the model they want to deploy and create new instances of the AI model for various projects.
How can the content safety filters be integrated in Azure AI Studio?
-Content safety filters in Azure AI Studio can be integrated to mitigate problems like harm, depending on the type of solution being built. This feature helps in ensuring that the AI's responses are appropriate and safe for the intended use.
What is the significance of the 'View code' feature in the playground?
-The 'View code' feature in the playground provides a pre-made prompt that can be used within code. It allows users to see an example of how to integrate the API into their projects in different programming languages, making it easier to implement the AI model into their applications.
How can one access more information and documentation on Azure AI Studio?
-More information and documentation on Azure AI Studio can be accessed through the provided links in the video description. This includes access to the project's repository, sign-up information for Azure AI Studio, and detailed guides on how to use the platform.
Outlines
🚀 Introduction to Azure AI Studio and Its Capabilities
The video begins by introducing Azure AI Studio, a comprehensive suite of tools for developers aiming to build complex applications. It outlines the five main parts of the video: exploring Azure AI Studio, building models with Azure Open AI, importing data into models, configuring and deploying models, and understanding the API for project use. The speaker expresses gratitude to Microsoft for sponsoring the video and emphasizes the integration of various AI services. Azure AI Studio is described as a central hub for deploying models, testing them, adding custom data, defining prompts, and applying content safety filters. The variety of models available and the process of signing in and creating a new project are also covered.
📚 Exploring Azure AI Studio Features and Customization
This paragraph delves into the unique features of Azure AI Studio, such as the prompt flow for visual representation of complex prompts, and the ability to manage data sources. It demonstrates how to create a prompt flow, add data sources like Azure AI search, Azure blob storage, and uploaded files to enhance model performance with custom data. The process of uploading a document, indexing it, and using it to inform the AI's responses is shown. Additionally, the paragraph covers how to view raw inputs and outputs, switch between different modes like chat and completions, and how to deploy and manage instances of AI models in Azure AI Studio.
🔍 Deploying Models and Accessing Azure OpenAI Service
The speaker discusses the process of deploying models in Azure AI Studio, including the need to request access to certain models from the Azure OpenAI service team. After approval, models can be deployed and used immediately. The paragraph also explains how to switch between different model versions in the playground and how to enable enhancements like adding Vision capabilities to a model. Furthermore, it covers how to use the API for a coding project, providing a step-by-step guide on obtaining necessary keys and endpoints, and how to use them in a JavaScript code example.
💻 Using Azure AI Studio API for Coding Projects
The final paragraph focuses on the practical application of the Azure AI Studio API within a coding project. It guides the viewer through setting up an environment to use the API, including installing necessary packages and configuring environmental variables for the API key, endpoint, and model. The process of defining a prompt and executing a function to interact with the API is demonstrated. The speaker also shows how to run the function and receive a response, and encourages further learning through provided links to the project repository and Azure AI Studio documentation. The video concludes with an invitation for viewers to suggest topics for future videos on Microsoft Azure OpenAI.
Mindmap
Keywords
Azure AI Studio
Azure Open AI
Model Deployment
Data Import
Prompt Flow
Content Safety Filters
API Usage
GPT Model
Azure Subscription
AI Settings
Chat Completion
Environment Variables
Highlights
Azure AI Studio is a comprehensive tool for building complex AI solutions, suitable for both beginners and professional developers.
The platform integrates various Microsoft AI services, including Azure OpenAI, machine learning, speech, and vision services.
Users can deploy models, test them on Azure OpenAI, and add custom data for improved prompting.
Azure AI Studio features a unique prompt flow that allows users to create and customize models and their functionalities.
Content safety filters can be integrated to mitigate potential harms depending on the solution being built.
Multiple AI capabilities can be combined to produce advanced generative AI solutions.
The Azure AI Studio website hosts a variety of models from different sources, including Meta and Microsoft.
To use Azure OpenAI models, users must sign in with their account and select the desired model.
A new project can be created within the studio dashboard, where models can be selected and deployed.
The playground area of Azure AI Studio allows users to set up and test their AI environment with system messages and variables.
Variables can be used within system prompts and chat prompts, aiding in application development.
Playground settings enable users to select the AI language, subscription, and optionally add a speech resource.
The mode can be switched between chat, completions, or images depending on the model's capabilities.
JSON view provides raw inputs and outputs for API interactions, useful for programming integration.
Prompt flows visually represent complex processes, allowing customization and understanding of the underlying code.
Data can be added to the model from various sources like Azure AI search, Azure blob storage, or by uploading a file.
Uploaded documents can be used as a data source for the model to provide context-specific responses.
The data section in components allows users to manage and view the current version of their uploaded data.
Custom data sources in prompt flows reveal the complex prompts that occur, which is beneficial for learning prompt engineering.
Deployments can be managed within Azure AI Studio, with options to create new instances or select existing ones.
Access to certain models may require a request to the Azure OpenAI service team, demonstrating a controlled access model.
The API can be used for projects, with the ability to view code examples in different programming languages.
Environmental keys and endpoint URLs are essential for integrating Azure OpenAI models into coding projects.
The video provides a practical example of deploying a real-time model and integrating it with a JavaScript project.
The presenter offers additional resources and encourages viewers to explore Azure AI Studio and its documentation for further learning.