• #6 - Unlocking Business Value with Vector Databases – A Deep Dive

  • 2024/10/30
  • 再生時間： 36 分
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#6 - Unlocking Business Value with Vector Databases – A Deep Dive

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  • This episode of the AI Demystified for Executives podcast, presented by Dragonfly Risings School of Data and hosted by Andrew Psaltis, dives deep into the concept of vector databases. Discussing their importance in applying AI to business contexts, the episode explores the fundamentals of vector databases, their embedding models, and their applications across various industries. It highlights the increasing relevance of vector databases in AI-powered search and personalization, the impact of unstructured data, and the semantic meaning preserved in vector embeddings. Practical business use cases such as Walmart's search capabilities and Morgan Stanley's AI assistant are illustrated, along with potential challenges and considerations for integrating vector databases into existing systems. The episode emphasizes the transformative potential of vector databases in enhancing customer and employee experiences through improved search and recommendation systems.
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This episode of the AI Demystified for Executives podcast, presented by Dragonfly Risings School of Data and hosted by Andrew Psaltis, dives deep into the concept of vector databases. Discussing their importance in applying AI to business contexts, the episode explores the fundamentals of vector databases, their embedding models, and their applications across various industries. It highlights the increasing relevance of vector databases in AI-powered search and personalization, the impact of unstructured data, and the semantic meaning preserved in vector embeddings. Practical business use cases such as Walmart's search capabilities and Morgan Stanley's AI assistant are illustrated, along with potential challenges and considerations for integrating vector databases into existing systems. The episode emphasizes the transformative potential of vector databases in enhancing customer and employee experiences through improved search and recommendation systems.
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