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Learn why a vector database is critical to your AI strategy

 


Generative AI is revolutionizing data and analytics, but its applications require advanced data management capabilities to handle vast, diverse, and complex datasets including images, video, audio, documents, and text. Traditional databases, originally designed for structured data and exact matches, have proven inadequate for genAI models, which operate in high-dimensional spaces and often require similarity searches.

Vector Database is an advanced database designed for optimized storage and retrieval of high-dimensional vector data. It excels at performing large-scale similarity searches and streamlining data management for cutting-edge AI applications. A key benefit is its support for specialized vector indexes, which enable fast query processing and provide the high performance required for analyzing complex data.

We'll be taking a deep dive into Vector Databases at Forresters' Technology & Innovation Summit North America on September 12. Data professionals can gain valuable insights into leveraging Vector Databases to improve their AI strategies and implement industry best practices. This session will delve into the distinct benefits and practical applications of Vector Databases, highlighting their vital role for organizations working to optimize their AI strategies.

Below is a preview of some of the topics we will cover in the session.

Distinctive features of vector databases. Unlike traditional databases, vector databases excel at efficiently storing and retrieving complex vector data generated by providers such as OpenAI, Hugging Face, and Cohere. The database allows for rapid similarity searches by indexing vectors. We discuss their unique advantages over traditional databases. Choosing between native and multimodal vector databases. Native vector databases are specifically built to efficiently manage large, complex, multidimensional vector data. Multimodal databases, on the other hand, have built-in vector features, including storage, indexing, and query capabilities. The presentation analyzes the strengths and limitations of both native vector databases and multimodal databases with vector support. Examining diverse use cases for vector databases. As interest in models that leverage complex, high-dimensional data, especially in generative AI applications, continues to grow, vector databases are gaining attention with a host of new use cases. Currently dominated by search augmentation generative (RAG), we expect to expand into non-RAG applications in the near future. We explore various use case scenarios in this evolving market and discuss future developments.

Don't miss out: We'll have more details at the Technology & Innovation Summit North America, so check out the agenda and reserve your seat.

Forrester clients can also get a broader perspective on how AI will impact data analytics by registering for the webinar, “AI Unleashes a Data Renaissance,” on July 25. The webinar is part of the AI ​​Advantage webinar series for clients.

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2/ https://www.forrester.com/blogs/why-is-vector-database-critical-for-ai-strategy-find-out-at-the-technology-and-innovation-summit-2024/

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