Build your own Copilot with Azure AI Studio | BRK201HG
TLDRIn the BRK201HG session, John Montgomery and Nabila Babar from the Azure AI team at Microsoft introduce Azure AI Studio, a platform designed to build custom AI copilots. The session highlights the capabilities of generative AI, emphasizing the importance of data and model choice for creating effective AI applications. Azure AI Studio offers a unified experience for deploying, managing, and evaluating AI models, with features like Azure AI Search for data grounding, multimodal interactions, and fine-tuning with models from OpenAI and other providers. The platform also addresses safety and responsibility in AI development with tools like Azure Content Safety Service and a comprehensive development lifecycle approach, including model benchmarking and the Prompt Flow tool for prompt orchestration and evaluation. The session demonstrates the potential of Azure AI Studio to revolutionize information access, content generation, and customer experiences through AI.
Takeaways
- 🚀 **Azure AI Studio** is a platform designed to build custom AI copilots that serve specific business needs, enhancing creativity and efficiency.
- 📈 **Generative AI** is transitioning from a niche technology to a widely accessible tool for businesses, offering a competitive AI advantage.
- 🤖 **Copilots** are emerging as a distinct category within AI, aimed at improving customer and employee experiences through tailored AI solutions.
- 🛡️ **Privacy and Security** are foundational in Azure AI Studio, ensuring that customer data is protected and used responsibly.
- 🌐 **Multimodal Interactions** are a focus, moving beyond text to include interactions that feel more natural, like image and video processing.
- 📚 **Data Integration** is crucial for training models, and Azure AI Studio facilitates the connection with various data sources for enriched AI outcomes.
- 🔍 **Azure AI Search** is highlighted for its ability to combine vector search with semantic and keyword search, enhancing the search capabilities of AI models.
- 📈 **GPT-4 Turbo** and other advanced models like **DALL·E 3** are now available through Azure OpenAI Service, offering higher efficiency and lower costs.
- 🔧 **Fine-Tuning and RAG** (Retrieval-Augmented Generation) are complementary approaches, with fine-tuning being more suitable for adjusting model tone and output format.
- 🤝 **Models as a Service** is a new offering that allows customers to use AI models without managing infrastructure, through partnerships with companies like Meta.
- 📊 **Benchmarking Tools** within AI Studio will enable customers to compare different models based on their specific needs and datasets for optimal model selection.
Q & A
What is the shift in generative AI technology that is being discussed?
-The shift in generative AI technology being discussed is its transition from a niche technology used by a few to an accessible technology that businesses are harnessing to benefit a broader audience.
What is the role of Copilots in the context of AI?
-Copilots are becoming an AI category in their own right, serving as custom AI assistants tailored to serve the exact needs of customers and employees, enhancing content generation, designing contextual experiences, and allowing people to focus on high-value work.
What is Azure AI Studio and what can it be used for?
-Azure AI Studio is a platform where users can build custom AI copilots. It allows users to apply state-of-the-art and open-source models, ground responses in their data with privacy protection, deliver multimodal interactions, and build on a foundation of trust through every step of app development.
How does Azure AI Studio help in creating multilingual responses?
-Azure AI Studio enables multilingual responses by allowing the integration of language models that can understand and generate responses in different languages, even when the product documentation is in a different language.
What is the significance of the Azure OpenAI Service mentioned in the script?
-Azure OpenAI Service is significant as it is used by 18,000 customers to build generative experiences. It powers everything from simple chat experiences to sophisticated applications like Microsoft copilots and internal applications for companies like Siemens.
How does Azure AI Studio support the development of generative AI applications?
-Azure AI Studio supports the development of generative AI applications by providing a unified platform that integrates multiple AI services, offering tools for data connection, model selection, prompt engineering, and deployment. It also includes safety and security features to ensure responsible AI development.
What are the benefits of using Azure AI Search for grounding AI models with company data?
-Azure AI Search allows for the grounding of AI models with company data by using a sophisticated ingestion pipeline that processes documents, chunks the data, and embeds it through an LLM. This enables the model to provide accurate answers specific to the company's data and improves the quality of the AI's responses.
How does Azure AI Studio facilitate the process of fine-tuning AI models?
-Azure AI Studio facilitates fine-tuning of AI models by allowing users to bring in their data, fine-tune the model with compute resources, and then assemble the new weights with the base model using Low-rank Adaptation. It also provides tools for evaluating the fine-tuned models and comparing them against others.
What is the role of Azure Content Safety Service in ensuring responsible AI development?
-Azure Content Safety Service plays a crucial role in responsible AI development by identifying potentially harmful content such as hate speech or sexual content. It helps ensure that the AI applications built are safe and secure, aligning with Microsoft's principles for safe and responsible AI.
How does Azure AI Studio help in managing the development lifecycle of AI applications?
-Azure AI Studio helps manage the development lifecycle of AI applications through features like Prompt Flow, which is used for prompt orchestration, evaluation, and engineering. It provides a complete set of tools for understanding model behavior, evaluating outputs, and ensuring the non-deterministic nature of LLMs is properly managed.
What is the significance of the Models as a Service offering in Azure AI Studio?
-Models as a Service in Azure AI Studio is significant as it allows customers to use AI models without dealing with the underlying infrastructure. It offers a pay-as-you-go service where customers can simply call an API endpoint, and the model is ready for use, simplifying the process of integrating AI into applications.
Outlines
🚀 Introduction to Generative AI and Azure AI Studio
The video opens with an introduction to the evolution of generative AI, highlighting its transition from a specialized technology to a widely accessible tool for businesses. It emphasizes the importance of harnessing AI responsibly and securely. The concept of 'copilots' as a new AI category is introduced, which are custom AI models designed to enhance creativity, user experiences, and enable humans to focus on high-value tasks. Azure AI Studio is presented as a comprehensive platform for building these custom AI copilots, offering state-of-the-art models, privacy protection, and a foundation of trust throughout app development. John Montgomery and Nabila Babar from Microsoft's Azure AI team discuss the capabilities of Azure OpenAI Service and its adoption by 18,000 customers for creating generative experiences. They also highlight a customer story from Perplexity, a company using Azure AI to revolutionize online information access.
🌟 Exploring Azure AI Studio and its Capabilities
Lauren Yang from Perplexity discusses the company's use of Azure OpenAI Service to control and modify production traffic models, starting with a Slack bot that evolved into a full-fledged product. Aravind Srinivas explains the safety and security measures of Azure, emphasizing the cost-saving benefits of the Pay-As-You-Go model. Denis Yarats details the power of large language models and the ability to quickly prototype and deploy features. The video then transitions into a demonstration of Azure AI Studio, showcasing how to create a project, connect AI-related assets, and manage deployments. It also illustrates how to integrate custom company data for model fine-tuning and enable multilingual responses.
🔍 Deep Dive into Data and Model Selection in Azure AI Studio
John Montgomery focuses on the importance of data quality for AI outcomes, emphasizing the connectivity of Azure AI Studio to various data sources. He discusses the significance of vector search and the introduction of Azure AI Search for semantic and keyword search capabilities. Nabila Babar provides a detailed explanation of the search process, including the ingestion pipeline and the three search options available in AI Studio: vector search, keyword search, and hybrid search with semantic ranking. She demonstrates the effectiveness of Azure AI Search in understanding user intent and retrieving relevant information.
📈 Multimodal AI and Data Sovereignty with Azure OpenAI Service
The video showcases the integration of GPT-4 Turbo, GPT-4 Turbo with Vision, and DALL·E 3 into Azure OpenAI Service, highlighting the advancements in multimodal AI capabilities. John Montgomery discusses the concept of multimodality, which extends language models to include video and image processing. He assures viewers about data sovereignty, stating that customer data remains their own and is not used for training foundation models. The video also introduces the Customer Copyright Commitment, which offers legal protection for applications built on Azure OpenAI Service that follow responsible guidelines.
🤖 Fine-Tuning, Models as a Service, and Benchmarking
John Montgomery explains the difference between fine-tuning and retrieval-augmented generation, noting that fine-tuning is more suitable for adjusting the tone or format of model outputs. He announces the private preview of GPT-4 Turbo for fine-tuning and introduces the Models as a Service concept, which allows customers to use AI models without managing infrastructure. Partnerships with Meta, Mistral, Jais, and Cohere are mentioned to bring their models to the platform. A model benchmarking system within AI Studio is also announced, which will allow users to compare different models based on their specific tasks.
🌐 Multimodality and the Future of Generative AI
Nabila Babar demonstrates the multimodal capabilities of Azure AI Studio, showcasing how it can generate descriptions and images for product descriptions and websites using DALL·E 3. She also illustrates the power of GPT-4 Turbo to analyze a video and generate a half-day itinerary and packing list. Additionally, Azure AI Speech is highlighted for enabling text-to-speech functionality, and Custom Neural Voice is introduced for creating natural-sounding synthetic voices. The video emphasizes the importance of multimodality in the future of generative AI applications.
🛡️ Safe and Responsible AI with Azure AI Studio
John Montgomery discusses Microsoft's principles for safe and responsible AI, which are integrated into their engineering processes. He introduces Azure Content Safety for identifying inappropriate content and the AI dashboard for evaluating AI applications. The video also covers the full development life cycle, including prompt orchestration and engineering through Prompt Flow, which is now generally available. The integration of AI Studio with Azure Machine Learning is discussed, along with the future of deploying models on the Edge through Windows AI Studio.
📚 Prompt Flow for Development, Evaluation, and Deployment
Nabila Babar demonstrates the use of Prompt Flow for managing the complexity of LLM applications. She shows how developers can use Prompt Flow to iterate on code, run evaluations, and trace API calls. The video highlights the ability to perform detailed evaluations using various metrics, including data science and LLM-assisted metrics. Nabila also discusses the deployment process, monitoring, and enabling content filtering for safe application deployment. The video concludes with an invitation to explore the public preview of Azure AI Studio and the anticipation of innovative applications built using the platform.
Mindmap
Keywords
💡Generative AI
💡Azure AI Studio
💡Copilot
💡Azure OpenAI Service
💡Data Sovereignty
💡Multimodal Interactions
💡Fine-tuning
💡Retrieval-Augmented Generation (RAG)
💡LLMOps
💡Azure Content Safety
💡Custom Neural Voice
Highlights
Generative AI is transitioning from a niche technology to an accessible tool for businesses, offering a competitive AI advantage.
Azure AI Studio enables the creation of custom AI copilots tailored to specific customer and employee needs.
Content generation is being integrated into all apps to enhance creativity and productivity.
Azure AI Studio provides a platform for building trust through every step of app development with privacy-protected responses.
Azure OpenAI Service is currently used by 18,000 customers to create generative experiences.
Perplexity is utilizing Azure AI Studio to revolutionize internet search, focusing on performance and speed.
Azure AI Search supports fine-tuning and orchestration flows, allowing for efficient data grounding.
The Azure platform offers multilingual capabilities, providing responses in different languages based on the query.
Azure AI Studio integrates multiple AI services, including custom and open-source models, into a unified experience.
GPT-4 Turbo and DALL·E 3 are now available through Azure OpenAI Service, offering higher efficiency and lower costs.
Azure Content Safety Service can identify and filter harmful content, ensuring responsible AI deployment.
Azure AI Studio includes a full end-to-end development lifecycle, merging DevOps, MLOps, and LLMOps processes.
Prompt Flow within Azure AI Studio assists in prompt orchestration, evaluation, and engineering for better model behavior understanding.
Azure AI Studio supports fine-tuning of models and provides a benchmarking system to compare model performances.
Models as a Service allows customers to use AI models without managing infrastructure, offering a pay-as-you-go model deployment.
Azure AI Speech and Custom Neural Voice enable text-to-speech functionality, creating natural-sounding synthetic voices.
Azure AI Studio is now in public preview, offering a comprehensive platform for building generative AI applications.
