A recent survey of data scientists and engineers revealed that over half (53.3%) of today’s machine learning (ML) teams are planning on deploying a large language model (LLM) application of their own into production “within the next 12 months” or “as soon as possible”. Perhaps even more startling, however, is the finding that nearly one in ten (8.3%) enterprise ML teams have already deployed an LLM application into production.
Companies of every size, stripe and sector are racing to develop LLMs of their own, and as a result, the data landscape is changing. One of the most significant of those changes is the growing use of vector data — a complex type of unstructured data that is critical for the training and development of LLMs and other AI applications. In light of this trend, we’ve seen a growing number of startups emerge offering dedicated vector database systems with the promise of superior performance, searchability, and scale.
However, according to Percona founder Peter Zaitsev, these kinds of dedicated vector database systems are, for most organizations, not the right fit.
“As vector data becomes more mainstream, dedicated, specialized vector databases are emerging in hopes of satisfying the growing demand,” Peter said. “But it’s important to keep in mind that these systems offer highly specialized capabilities at the exclusion of many other, equally important ones. That’s why, at the same time, we’re beginning to see solutions that aim to integrate vector search and other vector capabilities within mainstream databases. Whether through integrations or extensions, I expect that, for the lion’s share of enterprise users, these types of solutions will offer more cohesive, lightweight, and familiar solutions to their AI development needs.”
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