Artificial Intelligence (AI) has emerged as a game-changer. It’s transforming industries, streamlining operations, and creating new opportunities for innovation. One of the key players in this AI revolution is Microsoft Azure, a cloud computing service created by Microsoft.
Azure AI Hub, a pivotal component of Microsoft Azure, offers a plethora of resources that can help businesses unlock the full potential of AI. This article aims to guide you through these resources, demonstrating how they can serve as a pathway to efficient AI solutions.
What is Azure AI Hub?
Azure AI Hub is a top-level Azure resource for AI Studio that provides a collaborative environment for a team to build and manage AI applications. It enables access to multiple Azure AI services with a single setup.
In Azure, resources enable access to Azure services for individuals and teams. Resources also provide a container for billing, security configuration, and monitoring. The Azure AI hub resource can be used to access multiple Azure AI services with a single setup.
Keys and Capabilities of Azure AI Hub
Here are some of its key features and capabilities:
Collaboration Environment
The Azure AI hub is where teams come together to build and manage AI applications. It serves two main roles:
For AI Developers: The Azure AI hub offers a workspace tailored for creating AI applications.
Developers have access to a variety of tools for building AI models. These tools can be used together to create and share components like datasets, indexes, and models.
Additionally, developers can configure connections to external resources and access prebuilt AI models using keys provided by the Azure AI hub. Projects hosted within the Azure AI hub can utilize these shared resources.
For IT Administrators: The Azure AI hub provides a unified view of all team projects, allowing administrators to monitor connections to external resources and enforce governance controls to ensure compliance and manage costs.
Organize your work
Several users can work on a project hosted by the Azure AI hub resource, and likewise, a user can contribute to multiple projects. Projects help in tracking billing, managing access, and ensuring data privacy. Each project has its storage space for uploading files, which can be shared exclusively among project members during data-related tasks.
Moreover, projects feature specific settings unique to each project:
Project Connections: Links to external resources like data storage providers accessible only to project members, complementing shared connections on the Azure AI hub resource.
Prompt Flow Runtime: A runtime environment required for using prompt flow, a feature facilitating the generation, customization, or execution of flows atop a compute instance.
The Azure AI hub resource provides access to prebuilt AI services through API endpoints and keys. The availability of services depends on your Azure region and the chosen Azure AI services provider during setup:
If you create an Azure AI hub resource with an existing Azure OpenAI Service resource, you'll only have access to Azure OpenAI Service capabilities.
If you create an Azure AI hub resource with an Azure AI services provider, you can access Azure OpenAI Service along with other services like Speech and Vision.
With the same API key, you can access these Azure AI services:
Azure OpenAI: Handles various natural language tasks.
Content Safety: Detects unwanted content.
Speech: Converts speech to text, text to speech, translates, and recognizes speakers.
Vision: Analyzes content in images and videos.
Large language models hosted by the Azure AI hub resource can generate text, speech, images, and more. These models are isolated within projects.
Virtual Networking
Azure AI hub resources, compute resources, and projects share the same virtual network managed by Microsoft. Once networking settings are configured during setup, all new projects created under the Azure AI hub resource inherit these settings. Default access is through the public network.
For private inbound connections to your Azure AI hub resource environment, create Azure Private Link endpoints for:
The Azure AI hub resource.
Dependent Azure AI services.
Other Azure AI dependencies like storage.
Projects appear as tracking resources in the Azure portal but don't need separate private link endpoints for access. New projects created after Azure AI hub resource setup are automatically added to the network-isolated environment.
Connections to Azure and other third-party resources
Azure AI provides connectors to link with various data sources and Azure tools. These connectors allow you to access data like indices in Azure AI Search to enhance your workflows.
Connections can be shared among all projects within the same Azure AI hub resource or created exclusively for individual projects.
To manage project connections through Azure AI Studio, go to the project page and navigate to AI project settings > Connections.
For shared connections, go to the Manage page. Administrators can review both shared and project-specific connections at the Azure AI hub resource level for comprehensive connectivity oversight across projects.
Azure AI Dependencies
Azure AI Studio builds upon existing Azure services, including Azure AI and Azure Machine Learning services. Although these details might not be apparent in the Azure portal or AI Studio interface, they become evident when interacting with Azure REST APIs, cost reporting, or infrastructure-as-code templates.
When creating a new Azure AI hub resource, several dependent Azure resources are needed to store data generated or uploaded in AI Studio. If not provided, these resources are automatically created. These resources include Azure Search for search capabilities, Azure Storage for storing artifacts, Azure Key Vault for storing secrets like connection strings, Azure Container Registry for docker images used with custom runtime, and Azure Application Insights & Log Analytics Workspace for log storage.
Working with Azure AI Hub
To utilize Azure AI Hub, you first need to create a resource and then a project within that resource. Here are the steps to guide you through this process:
Steps to Create Azure AI Hub Resource
Follow the below steps to create an Azure AI Hub Resource:
STEP 1: Open the Azure AI Studio. Look for the "Manage" section in the navigation pane.
STEP 2: In the `Manage` section, you will find an option labeled `+ New AI Hub`. Click on this to start the process of creating a new AI Hub.
STEP 3: You will now be prompted to enter several pieces of information:
Azure AI Hub Resource: This is where you provide a unique name for your AI Hub.
Azure Subscription: Enter your Azure subscription.
Resource group: Select the resource group where you want your AI Hub to reside.
Location - Choose from the supported regions where you want your AI Hub to be located.
Azure OpenAI - Here, you have the option to select an existing Azure OpenAI resource. This will bring all your deployments into Azure AI Studio.
Azure AI Search (Cognitive search): You also have the option to connect an existing Azure AI Search instance. This will allow you to share search indices with all projects in this Azure AI Hub Resource. However, this step is optional.
Once you've entered all the required information, click `Next`.
STEP 4: Click `Create`. This will finalize the creation of your new AI Hub.
After completing these steps, you should see an overview of your newly created Azure AI Hub.
Steps to Create an AI Project
After successfully creating a resource in Azure AI Studio, the next step is to create an AI project. This project will serve as the workspace where you can build, train, and deploy your AI models.
Here’s how you can do it:
STEP 1: Start by clicking on the `Build` option in the navigation pane of Azure AI Studio.
In the `Build` section, you will find an option labeled `+ New AI project`. Click on this to start the process of creating a new project.
STEP 2: Enter the below credentials:
Project name: This is where you provide a unique name for your project.
Resource - Here, you will select the Azure AI Hub resource that you created earlier. This resource will host the project.
STEP 3: Once you've entered all the required information, click `Create an AI project`.
Access the Project Tools and Settings
After the project is created, you can access various tools, components, and project settings assets in the left navigation panel. These resources can be used to build, manage, and deploy your AI models.
Playground: The Playground is a tool where you can test and experiment with your AI models in real-time. It allows you to input different prompts and see how your model responds. This helps you understand the model’s behavior and capabilities.
Evaluation: This tool is used to assess the performance of your AI models. It allows you to run tests on your models and generate metrics, which provide insights into how well your model is performing. These insights can help you identify areas where your model might need improvement. This is crucial for ensuring your model meets the necessary standards before it’s deployed.
Click on "+ New evaluation".
Prompt Flow: This tool helps you design and manage the conversation flow of your AI models. You can define the prompts that your model will use to interact with users and specify the responses your model should generate. This is essential for creating engaging and natural conversations with your AI models. It helps in making the interaction with the model more human-like and intuitive.
Click on "+ Create".
Fine-Tuning: This process allows you to adjust the parameters of your AI models to improve their performance. With the Fine-Tuning tool, you can tweak your model’s settings to better align with your specific use case or requirements. This is key for optimizing your model’s performance and ensuring it delivers the best possible results. It helps in customizing the model according to your specific needs and requirements.
Conclusion
the Azure AI Hub resource is a powerful tool that provides a comprehensive and collaborative environment for building and managing AI applications. Its integration with various Azure services, robust security features, and project hosting capabilities make it an indispensable asset for any team working on AI projects.
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