Free Research Note
More than Vectors: How Multi-Faceted AI Databases Enable Smart Applications
This research note explores the emergence of versatile AI databases that support multi-model applications. Practitioners, data/AI leaders, and business leaders should read this report to understand this new platform option for supporting modern AI/ML initiatives.
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Download this research note to learn:
- What AI databases are
- Why companies should consider adopting them
- How AI databases work
- What role they play in AI/ML architectures
- What use cases to consider and how to get started
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Further information
The AI database manages objects such as tables and documents, as well as vectors that assign numerical values to chunks of unstructured data such as words, images, audio clips, or video segments. The AI database can apply one or more various AI models to each piece of data, whatever the format, and combine multiple signals to create more accurate AI outputs. For example, a GenAI chatbot might calibrate its text outputs based on the results of a machine learning or natural language processing model.
By consolidating models and data types in one multi-purpose platform, the AI database improves computing efficiency and scalability compared with disparate single-purpose platforms.
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