Azure AI Studio (formerly called Azure OpenAI Studio) is the web interface that allows direct interaction with the Azure OpenAI service and has been specifically designed to experiment with the capabilities of OpenAI models. The interface offers a variety of features such as a chat environment for personalized AI assistants and completion models for generating a wide variety of content, placing an emphasis on ease of use for experimenting with different models and making it ideal for exploring the potential of AI, without requiring extensive technical knowledge. In this article we will see in more detail what it is, how it works, with which models it allows you to interact and how.
The world of corporate digital workplaces is changing face day by day at the pace of progress in the field of AI and, to remain at the forefront of corporate digital markets, Microsoft has also decided to invest heavily in the artificial intelligence market and in 2019 it became one of OpenAI's biggest partners, with an investment of more than one billion dollars.
One of the fruits of this partnership is Azure OpenAI, which allows companies and developers to integrate advanced artificial intelligence models developed by OpenAI into their applications through the cloud infrastructure of Microsoft Azure and, to take advantage of and experiment with the functionality offered by AI models, Microsoft has made Azure AI Studio available to users.
Azure AI Studio is a web interface with a simple and intuitive design, designed by Microsoft for easy use by any group of users and that allows you to touch and test the potential of the latest discoveries in the field of Artificial Intelligence (such as the now famous GPT-3.5, GPT-4 models), without the need for extensive knowledge on the subject.
This interface offers Azure users who want to make the most of artificial intelligence services a unique environment to explore and implement AI solutions quickly and intelligently.
Azure AI Studio is built around a series of design principles that aim to make artificial intelligence accessible, versatile and integrated into the user's workflow. With its focus on user experience, ease of experimentation and accessibility for education and personalization, the platform represents a powerful tool for anyone who wants to explore and apply the potential of Artificial Intelligence.
At the base of Azure AI Studio is the concept of 'playground' (literally 'playground'), i.e. interactive and intuitive spaces where users can freely experiment with the artificial intelligence models made available by the service. The playgrounds serve as virtual laboratories where developers, educators and creatives can rapidly innovate and prototype ideas, harnessing the power of advanced OpenAI models integrated into the Azure platform.
These environments are designed to facilitate the creation, customization and testing of AI applications without the need for advanced technical knowledge and guide users through a user-friendly interface through the process of configuring and using the models, allowing them to explore their capabilities.
All of these playgrounds are designed to be intuitive and easy to use, even for those without in-depth experience in programming or artificial intelligence, making experimentation accessible to a wide range of professionals and enthusiasts.
Integration with Azure infrastructure also allows for easy deployment of models in production environments. Users can configure, test, and deploy their AI projects using Azure cloud resources, benefiting from the platform's scalability, security, and management capabilities.
The Chat Playground is an interactive platform where users can design and test personalized virtual assistants using the GPT-3.5 and GPT-4 language models. This environment is especially useful for exploring the conversational abilities of natural language models.
In the Chat Playground, users can enter questions, prompts, or commands and observe how the model responds naturally and consistently. This tool is ideal for developing chatbots and digital assistants that can interact with users in a convincing way. Developers can configure prompts to simulate specific conversations, customize responses, and test various interaction scenarios.
A practical example would be the creation of a customer service assistant. Users can program the assistant to answer frequently asked questions, guide customers through support procedures, or provide information about products and services. The model's ability to understand and generate relevant responses makes this playground a powerful tool for improving customer interaction.
The Completion Playground allows users to experiment with generating text using GPT templates. In this environment, you can enter partial text or a prompt, and the template will complement the text in a consistent and relevant way.
This tool is particularly useful for a number of applications, such as automatically writing content, generating summaries, or creating answers in specific contexts. Users can use the Completion Playground to generate emails, product descriptions, and advertisements. The model's ability to understand the context and continue the text fluidly makes this playground ideal for improving efficiency in content production.
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GPT-4, acronym for “Generative Pre-trained Transformer 4", is the most advanced artificial intelligence model developed to date by OpenAI. The model was designed using the Transformer architecture, and was pre-trained on huge amounts of texts from the Internet and other sources, allowing it to learn language models and general knowledge through a vast body of data.
Within Microsoft Azure, the model is integrated to allow users to take advantage of these advanced capabilities through the intuitive interface of Azure AI Studio. This facilitates the exploration and application of GPT-4 functionality in a variety of contexts, from the development of virtual assistants to data analysis, expanding the possibilities of using artificial intelligence for business and creative solutions.
GPT-4 represents a significant advance in the artificial intelligence models provided by the service and one of its flagships, offering enhanced features and extended capabilities compared to its predecessor, GPT-3.5. Let's look at them better in the following list.
It should also be noted that the Azure version of ChatGPT is enriched with a content filter feature, absent in its public counterpart. This feature is crucial in preventing data leaks during chat sessions by filtering sensitive information, thus ensuring user privacy and the security of business data.
Azure OpenAI is powered by a diverse set of models with different capacities and price ranges, and their availability of models may vary depending on the geographical region. This is important to consider for companies that operate in different parts of the world or that have specific regional compliance requirements.
In this section, we will talk in general terms about the models made available by the service with which it is possible to interact through the Azure AI Studio interface and the cost of using them. To obtain a complete list of the models available for both inference and fine-tuning, ordered by version and region of availability, please consult the convenient table in the official Microsoft documentation dedicated to Azure OpenAI (available for consultation hither).
Obviously, we can only start with an overview of the GPT models, the main offer of the service.
GPT-4o (which we talked about in more detail in the previous section) is the latest model developed by OpenAI and integrates text and images into a single model, allowing it to simultaneously manage different types of data. This multimodal approach improves accuracy and responsiveness in human-computer interactions.
GPT-4 Turbo is its most advanced version and is a large multimodal model (accepts text or image inputs and generates text) that can solve complex problems with greater precision than any previous OpenAI model. Like GPT-3.5 Turbo and previous GPT-4 models, GPT-4 Turbo is optimized for chat and works well for traditional completion tasks. However, its strength lies in reaching where its predecessors cannot and it is generally used for more specific tasks that require more complex and refined solutions.
Azure currently makes GPT-4 models available only in a few strategic regions, including:
The models GPT-3.5, although more outdated, they continue to be a popular choice for multiple applications thanks to their greater availability in more geographical regions and their ability to understand and generate natural language effectively at lower costs than GPT-4 models.
The most capable and affordable model of the GPT-3.5 family made available by the service is GPT-3.5 Turbo, which has been optimized for chat and also works well for traditional completion tasks.
For companies that are unable to implement the GPT-4 models or prefer an economic solution, it is generally recommended (if they want to use these models) to use GPT-3.5 Turbo and GPT-3.5 Turbo Instruct compared to the legacy GPT-3.5 and GPT-3 models.
Embedding models are tools designed to transform textual data into compact, information-rich numerical representations. These models are fundamental to many artificial intelligence applications, such as semantic search, document clustering, and improving the performance of machine learning models through size reduction.
An embedding is a vector representation of data, where each element is mapped into a continuous numerical space at reduced dimensions. This process allows us to capture and represent the semantics and relationships between data so that they can be used effectively in machine learning models.
text-embedding-3-large It is the last and most capable of these models and supports the reduction of the size of the embedding through a new size parameter. Larger embeddings are generally more expensive in terms of computation, memory, and storage. The ability to adjust the number of dimensions allows greater control over overall costs and performance.
The costs for using Azure AI models are largely dependent on the specific model, usage, and tokens processed during interactions, both for input and output.
Tokens are segments of text, with each token usually corresponding to about four characters in English. This costing system applies to various models, regardless of whether it's generating text completes or conducting conversations.
To give an example of how they work, if a user provides a prompt of 1,000 tokens and receives a response of 1,000 tokens, the total cost of the operation will be for 2,000 tokens. Newer models support higher token limits, improving their ability to handle more complex and lengthy interactions.
The cost of models such as GPT-3.5 and GPT-4 reflects their capabilities and the computational resources needed to use them. GPT-3.5 Turbo, optimized for chat, is often more convenient for applications that require frequent or prolonged interactions. GPT-4 Turbo, with higher performance, obviously involves higher costs but provides better results in solving complex problems and managing multimodal inputs (text and images).
Azure embedding models, such as text-embedding-ada-002 and the more recent text-embedding-3-large, cost based on the number of tokens processed. The new text-embedding-3-large offers configurable dimensions, allowing users to balance performance and cost.
Fine-tuning (limited to the basic GPT-3 series) involves three cost components: hours of training, hours of hosting and usage per token for inference. The hosting costs are continuous and are incurred regardless of active use, making it essential to monitor and carefully manage these distributions to avoid unnecessary expenses.
Customers should always be aware of potential additional costs such as data storage and monitoring services. Azure provides tools for cost analysis and budget management, allowing users to effectively track and manage expenses. Budgets and alerts can help avoid overspending by notifying users of spending anomalies or the achievement of cost thresholds.
For those who want some more specific details on the specific cost of the service, we invite you to make use of the tool provided by Microsoft hither to calculate it based on the region and the models you want to use.
Basically, Azure AI Studio represents an important step forward in making advanced artificial intelligence capabilities accessible and usable by a wider audience and makes the service offered by Microsoft one of the most convenient and effective solutions on the market for integrating the potential of artificial intelligence into its company's operations.
As we have seen, the intuitive interface design and the flexibility offered by model management allow users and companies of all sizes to experiment and deploy AI-based solutions quickly and efficiently, without the need for too in-depth technical knowledge.
All that remains is to invite you, therefore, to discover first-hand the potential of the service and of Azure AI Studio, experimenting with its interface to see with your own eyes how AI can transform your company's digital infrastructures and project it into the future.
Azure AI Studio is a Microsoft web interface designed to allow users to experiment with OpenAI models, including GPT-3.5 and GPT-4, within the Azure cloud infrastructure.
Azure AI Studio offers “playgrounds” for testing various AI features, allowing for easy setup and testing of personalized assistants and content generation tools without requiring advanced technical skills.
Azure AI Studio supports the development of conversational agents, text completion for content generation, embedding models for data clustering, and AI-based customer support solutions.
Azure AI Studio includes GPT-3.5, GPT-4 and embedding models, each suitable for different applications, from generating complex texts to embedding text for machine learning activities.
The costs depend on the type of model and usage, including the number of tokens processed per session, making Azure AI Studio scalable for different application needs.
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