What is Vector Search? Challenges & Issues in Scaling Explained
Ever wondered how search engines find the most relevant results in milliseconds?
In this video, I break down Vector Search using a clear and simple mind map.
In this session, I cover:
✅ What is Vector Search and why it matters
✅ How vectors are obtained from your data
✅ Choosing the right similarity metric
✅ Performing vector search for small vs large datasets
✅ Managed vs self-hosted solutions
✅ Popular tools like FAISS, HNSW, and Qdrant
Vector search powers modern AI applications, from semantic search to recommendation engines. But what happens when you scale it to millions (or billions) of vectors? In this video, we break down:
What vector search really is (in simple terms)
How it differs from traditional keyword search
The common scaling challenges (latency, memory, cost, infrastructure)
Real-world examples of vector databases in action
Whether you’re new to vector search or exploring advanced scaling strategies, this video will give you the clarity you need.
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#VectorSearch #AI #Databases #Scaling
Whether you're a data scientist, ML engineer, or just curious, this visual approach will make vector search easy to understand.
vector search, semantic search, faiss, hnsw, qdrant, embeddings, AI search, machine learning, ANN search, semantic similarity, nearest neighbor search