The rapid growth of machine learning, especially large language models, have led to a commensurate growth in the need to store and compare vectors. In this episode Louis Brandy discusses the applications for vector search capabilities both in and outside of AI, as well as the challenges of maintaining real-time indexes of vector data.
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- Your host is Tobias Macey and today I'm interviewing Louis Brandy about building vector indexes in real-time for analytics and AI applications
- How did you get involved in the area of data management?
- Can you describe what vector search is and how it differs from other search technologies?
- What are the technical challenges related to providing vector search?
- What are the applications for vector search that merit the added complexity?
- Vector databases have been gaining a lot of attention recently with the proliferation of LLM applications. Is a dedicated database technology required to support vector indexes/vector search queries?
- What are the use cases for native vector data types that are separate from AI?
- With the increasing usage of vectors for data and AI/ML applications, who do you typically see as the owner of that problem space? (e.g. data engineers, ML engineers, data scientists, etc.)
- For teams who are investing in vector search, what are the architectural considerations that they need to be aware of?
- How does it impact the data pipeline strategies/topologies used?
- What are the complexities that need to be addressed when updating vector data in a real-time/streaming fashion?
- How does that influence the client strategies that are querying that data?
- What are the most interesting, innovative, or unexpected ways that you have seen vector search used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on vector search applications?
- When is vector search the wrong choice?
- What do you see as future potential applications for vector indexes/vector search?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Thank you for listening! Don't forget to check out our other shows. The Machine Learning Podcast helps you go from idea to production with machine learning. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
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- Vector Index
- Vector Search
- Vector Space
- Euclidean Distance
- OLAP == Online Analytical Processing
- OLTP == Online Transaction Processing