Hi there, welcome to my blog! I'm Shivam Goyal, a passionate Microsoft Learn Student Ambassador who loves learning about Artificial Intelligence and Machine Learning. It's amazing how AI can be used to solve complex problems and make our lives easier. I'm fascinated by the potential of AI and I believe it will transform the way we live, work, and interact with the world around us.
Introduction
Artificial intelligence (AI) integration has become essential in our quickly changing technology landscape. Azure AI Studio proves to be a potent platform that makes it remarkably simple and effective to create, implement, and manage AI-based applications. Azure AI Studio provides a wide range of tools and capabilities to make it easy for you to develop, test, and implement AI applications, regardless of your experience level. Azure AI Studio (preview) brings together capabilities from Azure Machine Learning, Azure OpenAI service, and other Azure AI services to provide a single, centralized workspace within which developers can collaborate with data scientists and others to build AI solutions.
We'll go into the fundamentals of Azure AI Studio in this blog post, going over its salient features, assisting you in starting your first AI project, and offering tools to advance your education.
Enhanced Functionality and Scalability with Azure AI Studio:
- Azure AI Studio is a comprehensive platform for AI development projects, offering pre-trained models for quick project initiation and improved functionality and scalability.
- Its user-friendly interface simplifies navigation through intricate AI workflows, while its powerful tools cover every development stage from preprocessing to deployment.
- Developed with developers in mind, Azure AI Studio eases smooth team collaboration by enabling direct code interaction via the Azure AI SDK and CLI.
- It ensures long-term success by providing a smooth transition from proof of concept to production through ongoing monitoring and improvement.
- Expedite the development of GPT-powered AI assistants, you can use the Assistants API, which offers prebuilt conversation state management and customization tools.
- By combining speech and vision models with Azure OpenAI, you can enhance the multimodality of your apps and create more engaging interactions.
Exploring Azure AI Studio
Before starting with Azure AI Studio, it's important to have a basic understanding of the fundamental Artificial.
Intelligence services available on Azure. We recommend completing the Get Started with Artificial Intelligence Learning path first. Additionally, if you need to perform certain tasks such as using a generative AI model, you must have access to Azure OpenAI services. To gain access, customers are required to submit a registration form. Once you have the necessary knowledge and access, you can dive deeper into the world of Azure AI Studio.
Learn how to create your first generative AI model using Azure AI Studio.
Azure AI Hubs
An Azure AI hub is the foundation for AI development projects on Azure and enables you to define shared assets that can be used across multiple projects. You need at least one Azure AI hub to use the solution development features and capabilities of AI Studio.
- Navigate to the Manage page and select + New Azure AI Hub Resource. Then, in Create an AI hub, create a new Azure AI resource with the following config:
- Azure AI hub resource: A unique name
- Azure subscription: your default subscription
- Resource group: Create a new resource group with a unique name or select existing if any.
- Location: Choose the availability of a model-supported location
- Azure OpenAI: (New) azure-ai-hub
- AI Search: None (Optional)
- When you reach the Review and Finish page, you will be able to find the AI Services provider that grants you access to Azure's AI services, including Azure OpenAI. On creation, you will be able to Manage your AI hub page in AI Studio.
View the artifacts that can be created and managed, look at the Azure AI Hub's left-hand pane. Locate the pre-existing connection to your Azure OpenAI resource (called Default_AzureOpenAI), go to the Connections page. Notice a possibility to create a new AI project on the Details page.
Azure AI Projects
Projects in Azure AI Studio help you build custom AI apps while keeping your work organized and secure. The
Azure AI hub resource provides a collaborative environment and enterprise-grade security. Projects also support a structured workflow by acting as organizational containers. Let’s create a project in Azure AI Hub.
- In the Create a new project wizard, create a project with the following settings:
- Click on the Build tab in Azure AI Studio.
- Select + New project.
- Provide a unique name for your project.
- Choose an existing Azure AI hub resource or create a new one.
- Wait until your project is created. Here, my AI project name is Genesis :)
- Features and Capabilities of Azure AI Projects:
- Set up robust language models for your chatbot or copilot.
- Assess models in the chat playground.
- Improve responses by incorporating your data.
- Utilize prompt flow for dynamic interactions.
- Evaluate the replies generated by the model.
- Manage indexes and datasets.
- Establish content filters to avoid harmful outputs.
- Deploy solutions as web apps or containerized services.
Indeed, creating your assistant to perform tasks on your behalf is quite remarkable.
Model deployment and managing.
You can use a variety of generative AI models with Azure AI Studio to develop complex AI solutions. You can look through its model catalog and choose the model that best fits the requirements of your application. Additionally, you can quickly create a benchmarking dashboard that contrasts the functionality of two or more models in particular domains. To further tailor your Large Language Models) LLMs for vertical scenarios, you have access to a library of pre-built metapromts. Additionally, you can add to this library to make it better for your company. For flows and other resources, create a deployment from their respective list Go to the model catalog.
- Navigate to the Components section in the left pane of your project interface and select the Deployments page.
- Once on the Deployments page, click on + Create to initiate the creation of a real-time endpoint deployment.
- From the Select a model list, choose the gpt-35-turbo model (according to your convenience), and confirm your selection. Proceed to deploy the model using the following settings:
- Deployment name: Provide a unique name for your model deployment.
- Model: Select the gpt-35-turbo model.
- Model version: Choose the default version.
- Under Advanced options, configure the deployment with the following settings:
- Content filter: Set to Default.
- Deployment type: Choose Standard.
- Tokens per Minute Rate Limit (thousands): Set to 5K.
- After the deployment, navigate to Tools section and select the Playground page.
- In the chat section, enter any prompt message such as How Azure AI studio can be the future for AI development, any 5 pros?
Congratulations! You have successfully created your first AI project on Azure AI Studio. The platform offers numerous tools, models, and components to develop your applications.
- Note: cleaning up unused resources is essential to avoid unnecessary costs.
- Access the Azure Portal:
- Return to the Azure portal in your browser (or open a new tab if needed).
- Locate the resource group where you deployed the resources used in this exercise.
- Delete the Resource Group:
- Click on the resource group name to view its contents.
- On the toolbar, select Delete resource group.
- Enter the resource group name when prompted and confirm your intention to drop it.
- Access the Azure Portal:
Productize your solution as an app or service in Azure AI Studio.
Azure AI Studio provides a comprehensive platform with tools and capabilities for designing, evaluating, and implementing generative AI solutions within your project.
- Users can develop unique copilots and generative AI models using the platform.
- Deployment options include large language models (LLMs), flows, and web applications, suitable for production environments such as websites and applications.
- Models can be hosted on servers or in the cloud, with APIs or other interfaces provided for user interaction.
- Continuous assessment of behaviour and performance of deployed solutions is facilitated by Azure AI Studio, enabling necessary modifications.
- Scaling options are available to accommodate increased traffic as the service gains popularity while prioritizing performance, affordability, and user experience enhancements.
Resources
- What is Azure? | Microsoft Learn
- How to create and manage an Azure AI hub resource
- Create custom copilots with Azure AI Studio (preview) | Training Module
Thank you for taking the time to explore this introduction to Azure AI Studio. If you have any further questions or would like to connect for more discussion, feel free to reach out to me on LinkedIn.
