World's Fair 2024
Accelerate your AI journey with Azure AI model catalog: Sharmila Chokalingam
Overview
This talk introduces the Azure AI model catalog as a comprehensive platform for accelerating AI development. It highlights how the catalog provides access to a wide range of foundation models, including flagship LLMs and smaller models, alongside tools for prototyping, optimizing, and operationalizing generative AI applications. The platform emphasizes ease of model switching, enterprise-grade security, and data privacy to support production workloads.
Who should watch
- AI engineers looking to leverage a diverse set of foundation models.
- Product Managers seeking to build and deploy generative AI applications efficiently.
- Builders evaluating different models for specific use cases and performance needs.
- Teams concerned with data privacy, security, and responsible AI practices in their AI projects.
- Developers aiming to streamline the AI development lifecycle from prototyping to production.
Key takeaways
- The Azure AI model catalog offers a broad selection of models, including large language models (LLMs) like GPT-4, Mistral, Llama, and Cohere, as well as small language models (SLMs) and multimodal capabilities.
- Model choice is crucial for AI development, involving stages of prototyping, optimization (for cost, latency, etc.), and operationalization with features like prompt engineering, RAG, and fine-tuning.
- Azure AI Studio provides tools for easy model deployment, comparison via benchmarks, and testing through a playground interface, allowing users to integrate their own data for specialized responses.
- The platform standardizes APIs across models, enabling seamless swapping of models without extensive code changes, and supports integration with popular frameworks like LangChain.
- Function calling capabilities, demonstrated with the Mistral Large model, allow for intelligent interaction with external tools and data sources to provide precise information.
- Prompt flow enables the creation of generative AI applications, such as RAG-based chatbots, with features like query transformation and the ability to evaluate and compare different models within the same flow.
- Azure AI prioritizes data privacy and security, ensuring customer prompts and data are not shared with model providers or used for training, and offers features like private networking and Azure policy integration for compliance.
- Customer success stories illustrate the use of multiple models from the catalog for specific use cases, leading to enhanced productivity, reduced latency, and improved accuracy in applications like EY's internal AI platform and CGM's customer care chatbots.
Notable quotes
*The Azure AI model inference API makes it standard and easy for you to switch between multiple models.*
*Your prompts and your completions are not shared with the model provider nor your data is used for training any of the models.*
*The time it took for them to start using LLMs in their applications to seeing the results and impact has been reduced significantly.*
Unofficial community note. Prefer the recording for nuance.