Practically Intelligent Vector Databases, Embeddings, and a history of Deep Learning with Leo Dirac
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Jun 8, 2023 Former Engineering Lead behind Deep Learning at AWS, Leo Dirac, shares a walk through history and key takeaways for builders in the AI/ML space. They discuss the importance of vector databases, comparing different options, and the challenges of computer vision. Leo also talks about his new venture, Groundlight.AI, and its role in simplifying computer vision for engineering leaders.
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What Embeddings Actually Represent
- Embeddings map variable text inputs to fixed-length numeric vectors capturing semantic similarity.
- Use cosine distance to find related documents for retrieval and LLM context.
Choose DB By Cost, Latency, And UX
- Evaluate vector databases by cost, read/write latency, and storage efficiency.
- Also consider logical features like namespaces, access controls, and developer tooling for real-world operations.
Developer-First Tools Win Adoption
- Prefer vector DBs with simple SDKs, instant sign-up, and Python wrappers for quick prototyping.
- Choose developer-first products to test ideas before committing to complex self-hosted stacks.