Chris Durow
AI Consultant @ advancing analytics
Plymouth, United Kingdom
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I am an accomplished AI Engineer and Technical Leader specialising in the design and deployment of production grade Generative AI, and Agentic systems.
My focus has been on bridging the gap between complex technical execution and strategic business outcomes. I have a proven track record of leading and delivering innovative GenAI and agentic solutions for clients across sectors like insurance, healthcare, and finance, transforming complex challenges into scalable, commercially viable products. I am passionate about building cutting-edge solutions that deliver real, attainable value and am driven by continuous learning in the field of artificial intelligence.
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Wrapping Azure Open AI, Ai Search & Cosmos DB into an Azure Function
In this session, we will explore how to seamlessly integrate Azure OpenAI, AI Search, and Cosmos DB into a single, efficient Azure Function. Azure OpenAI provides cutting-edge language models that can be utilized for a wide range of AI-driven tasks. AI Search enhances search capabilities with AI-powered indexing and querying. Cosmos DB offers a globally distributed, highly scalable database service. By wrapping these powerful Azure services into an Azure Function, we can create a robust, serverless solution that leverages the strengths of each component.
Attendees will gain insights into the architectural design and implementation strategies necessary for integrating these services. We will delve into practical examples and use cases, showcasing how to build and deploy an Azure Function that harnesses the combined power of Azure OpenAI, AI Search, and Cosmos DB. This session aims to provide a comprehensive understanding of how to create scalable, efficient, and intelligent applications using Azure's advanced cloud services.
Stitching GenAI Pipelines with RAG in Microsoft Fabric
In the evolving landscape of data and AI, GenAI has emerged as a game-changer for organisations seeking to derive deeper insights from their data. Retrieval-Augmented Generation (RAG) is the ground-breaking technique of combining retrieval-based and generative AI models to seamlessly integrate and enhance data from multiple sources and reducing model hallucinations.
We will explore how RAG leverages GenAI to provide contextually enriched, high quality outputs. With a practical demonstrations, attendees will learn how RAG in Fabric can be implemented to solve complex data challenges. This session will cover the technical underpinnings of RAG, its application in various industries, and the measurable benefits it delivers along with practical guidance on where to get started.
This session is for data professionals, Fabric fans and Data Scientists aiming to harness the latest advancements in GenAI for enhanced data-driven outcomes.
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