What is vector database optimization in ML systems?

Vector DB optimization improves similarity search speed and accuracy at scale.

Optimization involves indexing strategies like HNSW or IVF, compression techniques like PQ (product quantization), and caching frequent queries. Trade-offs exist between recall, latency, and memory usage. Proper tuning is essential for production-grade RAG systems.