Скачать с ютуб Evolving Your Data Architecture for Trustworthy Generative AI в хорошем качестве

Evolving Your Data Architecture for Trustworthy Generative AI

Generative AI is pushing us to reevaluate, advance and even reshape our data stacks. Data and IT teams are being asked to scale data workloads and storage and integrate sources spanning diverse environments. All of this makes the concept of an agile data architecture a priority for generative AI implementation plans. The Progress® MarkLogic® Data Hub enables semantic searches for an AI-ready data architecture. Thanks to its multi-model and harmonization capabilities, the data hub pattern can also address data quality challenges and help improve data trustworthiness by retrieving the most relevant content to support generative AI use cases. Explore how a data hub architecture can lay the necessary foundation for harnessing generative AI. The team provides a demo on personalized care delivery showcasing a growing use case of LLMs—enabling non-technical end-users and SMEs to query data with natural language to solve complex decisions. You’ll learn about: Integrating siloed data for accurate input in generative AI-driven solutions Building scalable data flows and querying multi-model data as a single whole Enabling LLMs and semantic search with the MarkLogic Data Hub Delivering personalized care to diabetes patients with AI-powered decision support (Demo) Learn how a data hub can help you get started with your AI journey today.