Vertex AI Matching Engine - Vector Similarity Search

Published: 28 June 2022
on channel: ML Engineer

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Putting a similarity index into production at scale is a pretty hard challenge. It requires a whole bunch of infrastructure working closely together. You need to handle a large amount of data at low latency. It introduces you to topics like sharding, hashing, trees, load balancing, efficient data transfer, data replication, and much more.

Check out the notebook and the article on how to get started with Google Cloud Vertex AI Matching Engine

📓 Notebook: https://colab.research.google.com/dri...
📖 Article:   / all-you-need-to-know-about-google-vertex-a...

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▬ Timestamps ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬

00:00 Introduction
00:32 Statement
00:47 Use Cases
01:25 Embedding
01:47 Input
02:23 Types
02:54 VPC
04:05 Create Embeddings
06:50 Setup
07:00 VPC Setup
08:39 Create Index
11:58 Create Endpoint
13:00 Deploy Index
14:23 Update Index
15:10 Scale Index
16:46 Query
22:23 Bye

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