Azure AI Studio

Build, Evaluate and Deploy a RAG-Based Retail Copilot with Azure AI: Cedric Vidal and David Smith

Feb 6, 2025 · 1:57:58

David Smith, Cedric Vidal, and Miguel Martinez lead a hands-on workshop on building a production-level RAG-based retail copilot using Azure AI. They demonstrate how to build a chatbot backend that retrieves product information from Azure AI Search via vector embeddings and customer history from Cosmos DB, then augments the LLM prompt to generate grounded answers. The session covers using Azure AI Studio and Prompt flow to orchestrate the RAG workflow, deploying the flow as a managed endpoint, and evaluating quality with GPT-4 as a judge on metrics like relevance and groundedness. The speakers also explain the LLM Ops lifecycle for iterative improvement and compare Prompt flow with Semantic Kernel and AutoGen.

Accelerate your AI journey with Azure AI model catalog: Sharmila Chokalingam

Feb 6, 2025 · 23:14

Shubhi and Sharmila present the Azure AI model catalog as a platform offering over 1,600 models including GPT-4, Mistral, Llama, Cohere, and Phi3, with a standardized inference API enabling easy model swapping. They demonstrate deployment via serverless API (pay-per-token) and managed compute, emphasizing that customer prompts and completions are not shared with model providers or used for training. The platform includes model benchmarks, playground for RAG, and Prompt Flow for building generative AI apps with evaluation and variant comparison. Customer success stories include EY's EYQ chat adopted by 275,000 employees, CMA CGM's reduced response latency using Mistral, and Bridgestone's 30% reduction in forecasting errors with Nixtla's TimeGen.

Multi model multimodal and multi agent innovations in Azure AI: Cedric Vidal

Feb 6, 2025 · 28:56

Cedric Vidal, Principal AI Advocate at Microsoft, demonstrates Azure AI's multi-model, multimodal, and multi-agent capabilities in a session packed with live demos. He shows how GPT-4 Omni mixes text and vision to read handwritten French menus and translate them, and diagnoses infrastructure damage from photos for energy and insurance industries. A new video translation service lets him speak German, Spanish, Italian, and Japanese in his own voice while preserving tone, such as whispering or yelling. The Azure AI model catalog now offers 1,600 models, including serverless deployment options, and Phi-3 Vision, a small 3.8B parameter model running locally in a browser via WebGPU. He also demonstrates code interpreter analyzing a GPX file from a kitesurfing session, plotting a map with turn markers, and GitHub Workspaces preview generating a Java GUI from Python code.

AI Templates: Gabriela and Aishwarya

Feb 6, 2025 · 1:03:20

Gabriela de Queiroz, Aishwarya, and Pamela from Microsoft present AI templates for rapidly prototyping and deploying generative AI applications, demonstrating how startups can leverage Microsoft for Startups Founders Hub, including up to $150,000 in Azure credits and expert guidance. The workshop covers three templates: a simple chat app using GPT-3.5 Turbo, a Retrieval Augmented Generation (RAG) app on Postgres with hybrid vector and text search, and a RAG app on unstructured documents using Azure AI Search and Document Intelligence. Key insights include the importance of query rewriting, hybrid search over pure vector search, and streaming responses for better user experience. The templates are open source on GitHub and can be deployed via Codespaces, with a proxy provided to bypass Azure OpenAI approval delays.

Creating and scaling your own custom copilots with Azure AI Studio: Hanchi Wang

Feb 6, 2025 · 24:21

Hanchi Wang, Software Engineer Lead at Azure AI, introduces Azure AI Studio and Promptflow for creating and scaling custom copilots, focusing on tracing, evaluation, and monitoring. He demonstrates a chatbot app that uses the Assistant API with a sales data insight tool (natural language to SQL) and a code interpreter. With Promptflow's trace decorator, developers capture inputs, outputs, and LLM interactions, viewable in a local UI and shareable via Azure AI Studio. For evaluation, he shows synthetic test data sets, content safety evaluators, and custom evaluators like execution time, error rate, and SQL similarity, comparing models such as GPT-4 Turbo, Mistral large, and Phi-3. In production, monitoring dashboards in Application Insights track model duration, token usage (prompt vs. completion), and failure rates, enabling engineers to optimize performance and cost.