How to use Microsoft Azure AI Studio and Azure OpenAI models
TLDRThis video tutorial introduces Microsoft Azure AI Studio, a platform for building complex AI solutions. It covers the creation of projects, integration of Azure OpenAI models like GPT-4, importing data for model enhancement, configuring and deploying models, and utilizing APIs for project implementation. The video also demonstrates how to add custom data, use variables, and manage data sources, showcasing the platform's capabilities in creating advanced AI applications.
Takeaways
- ๐ Azure AI Studio is a comprehensive platform for building complex AI solutions, combining various Microsoft AI services.
- ๐ ๏ธ The video tutorial is divided into five parts: introduction to Azure AI Studio, building models with Azure OpenAI, importing data, configuring and deploying models, and using the API.
- ๐ Azure AI Studio allows users to deploy models and test them on the Azure OpenAI service, with the ability to add custom data for better prompting.
- ๐ Users can define prompts that work like a flowchart, enabling the creation and customization of models and their functions.
- ๐ The platform integrates multiple AI capabilities, such as content safety filters and advanced generative AI solutions.
- ๐ The Azure AI Studio website hosts a variety of models, including those from Meta and Microsoft, with options to preview and select models for use.
- ๐ Authentication is required to access certain Azure OpenAI models, and users may need to request access from the Azure OpenAI service team.
- ๐ Data can be imported from various sources like CSV, databases, or documents, and can be used to enhance the AI model's responses.
- ๐ The prompt flow feature in Azure AI Studio visually represents the AI's decision-making process, allowing users to understand and customize complex prompts.
- ๐ Deployment options in Azure AI Studio include creating web apps and accessing the model via API, with the ability to switch between different models and enable enhancements.
- ๐ ๏ธ The video provides a practical example of how to use Azure AI Studio's API in a JavaScript project, demonstrating the setup process and code implementation.
Q & A
What is the main focus of the video?
-The video focuses on explaining how to use Microsoft Azure AI Studio and Azure OpenAI models for building complex AI solutions.
What are the key components of Azure AI Studio?
-Azure AI Studio combines tools like Azure OpenAI, machine learning, and other AI services such as speech and vision into a centralized platform.
How can custom data be added to Azure AI Studio for better prompting?
-Custom data can be added using databases, files, documents, or web addresses to improve the prompting capabilities of the AI models.
What is the purpose of the prompt flow feature in Azure AI Studio?
-The prompt flow feature allows users to create and customize models and their functions in a visual, flowchart-like manner.
How can content safety filters be integrated in Azure AI Studio?
-Content safety filters can be integrated to mitigate issues like harm, depending on the type of solution being built.
What is the process for deploying a model in Azure AI Studio?
-To deploy a model, users need to give it a deployment name and connect it to one of their projects within the Azure AI Studio dashboard.
How can data sources be added to Azure AI Studio?
-Data sources can be added by selecting 'add your data', choosing a data source like Azure AI search, Azure blob storage, or uploading a file, and then following the steps to complete the process.
What types of models are available in Azure OpenAI?
-Azure OpenAI offers various models, including ones from Meta and Microsoft, with examples like the GPT 4 model featured in the video.
How can the API be accessed and used for a project?
-The API can be accessed through the deployments tab in Azure AI Studio, where users can obtain the target URL and key to use as an environmental key in their coding projects.
What is the significance of the 'view code' feature in the playground?
-The 'view code' feature provides a pre-made prompt in different programming languages, which can be copied and pasted into a project for easy integration and use of the API.
Outlines
๐ Introduction to Azure AI Studio and Its Capabilities
This paragraph introduces Azure AI Studio, a comprehensive suite of tools provided by Microsoft for developers at any level. The speaker plans to cover five main aspects of Azure AI Studio in the video: an overview of the platform, building models on Azure Open AI, importing data for models, deploying models, and understanding the API for accessing and using the models. The speaker expresses gratitude to Microsoft for sponsoring the video and encourages viewers to explore Azure AI Studio further through provided links. The paragraph emphasizes the integration of various AI services, such as machine learning and speech recognition, and the ability to customize models with custom data and content safety filters.
๐ Exploring Data Import and Prompt Flow in Azure AI Studio
The second paragraph delves into the data import process in Azure AI Studio, highlighting the ability to add data sources like Azure AI Search, Azure Blob Storage, and file uploads. It demonstrates how to upload a document and use it to enhance the AI's responses. The paragraph also introduces the Prompt Flow feature, which visually outlines the interaction between inputs, outputs, and the AI's processing. The speaker uses a chat with Wikipedia example to illustrate the complexity and scalability of the platform. The paragraph concludes with instructions on how to add and manage data sources and the ability to customize responses further.
๐ Deploying Models and Accessing the API in Azure AI Studio
This paragraph focuses on deploying AI models within Azure AI Studio and accessing them via the API. The speaker guides viewers through the process of selecting a model, requesting access, and deploying it for immediate use. The paragraph also explains how to switch between different models and enable enhancements like Vision. The speaker then demonstrates how to use the API in a coding project, providing a step-by-step guide on setting up the environment, installing necessary packages, and writing a script to interact with the AI. The paragraph concludes with a practical example of how to retrieve and display AI-generated content in a JavaScript environment.
๐ฏ Conclusion and Further Learning with Azure AI Studio
In the final paragraph, the speaker wraps up the video by reiterating the potential of Azure AI Studio and encouraging viewers to explore more through the provided links. The speaker mentions plans to cover more topics related to Microsoft Azure Open AI in future videos and invites viewer suggestions for specific topics or projects. The paragraph ends with a call to action for viewers to access the project's repository and sign up for Azure AI Studio to deepen their understanding and application of the platform.
Mindmap
Keywords
๐กMicrosoft Azure AI Studio
๐กAzure Open AI
๐กGPT-4 model
๐กPrompt Flow
๐กData Import
๐กDeployment
๐กAPI Access
๐กChat Completion
๐กVariable
๐กContent Safety Filters
๐กMultiple AI Capabilities
Highlights
Azure AI Studio is a comprehensive platform 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, providing a centralized workspace for AI model development and deployment.
Users can create and customize models using Azure OpenAI's GPT-4 and other advanced models, enhancing them with custom data for better performance.
Azure AI Studio allows for the import of data from various sources such as CSV files, databases, and even web addresses for more tailored AI interactions.
The unique 'prompt flow' feature enables the creation of AI workflows that function like a flowchart, offering a visual way to design complex AI interactions.
Content safety filters can be integrated to mitigate potential harm, ensuring a safer AI solution depending on the project's requirements.
Multiple AI capabilities can be combined to produce advanced generative AI solutions, surpassing the capabilities of individual models.
The platform provides a 'playground' for testing and refining AI models, with features similar to AI chat environments, allowing for real-time adjustments and feedback.
Variables can be used within system and chat prompts, making it easier to build applications that reference specific data or context.
Azure AI Studio supports switching between different models and enabling various enhancements like vision capabilities for the models.
Data sources can be added to a model, with options to upload files or connect to Azure AI Search or Azure Blob Storage for more extensive data integration.
Once data is uploaded and indexed, it can be used to provide context for AI responses, enhancing the accuracy and relevance of the model's output.
Prompt flows can be visualized, allowing developers to understand and customize the steps involved in generating AI responses.
Deployment options are available on Azure AI Studio, making it easy to put the AI models into production for real-world use.
API access is provided for integrating the AI models into external projects, with the ability to view code examples in different programming languages.
The platform's documentation and resources, including tutorials and examples, support users in learning and leveraging Azure AI Studio's capabilities effectively.