hands-on lab

Use Vertex AI Vector Search to Recommend Similar Products

Intermediate
1h 15m
6
5/5
Get guided in a real environmentPractice with a step-by-step scenario in a real, provisioned environment.
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Lab description

Vector Search can explore billions of items that are semantically similar or related. This service, which matches vector similarities, finds application in various scenarios, including recommendation engines, search engines, chat bots, and text classification. Vertex AI Vector Search is the foundation of Google products such as Google Search, YouTube, and Play.

This lab is based on a retail use case, where you will use Vertex AI Vector Search to index and search for vector representations of products created by Vertex AI's Embedding for Text. You will see how this can efficiently provide product recommendations to users.

Learning Objectives

Upon completion of this intermediate-level lab, you will be able to:

  • Understand Vector Search terminology and concepts
  • Create a Vertex AI Vector Search index and index endpoints
  • Use the Vertex AI Vector Search API to search for similar items or nearest neighbors in the index

Intended Audience

This lab is intended for:

  • AI Practitioners
  • Data scientists
  • Machine learning engineers
  • Google Professional Cloud Machine Learning exam candidates

Prerequisites

You should possess:

  • A basic understanding of the following:

    • Vertex AI
    • Python

The following content can fulfill the prerequisites:

Environment before
Environment after
About the author
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Logan Rakai
Lead Content Developer - Labs
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Logan has been involved in software development and research since 2007 and has been in the cloud since 2012. He is an AWS Certified DevOps Engineer - Professional, AWS Certified Solutions Architect - Professional, Microsoft Certified Azure Solutions Architect Expert, MCSE: Cloud Platform and Infrastructure, Google Cloud Certified Associate Cloud Engineer, Certified Kubernetes Security Specialist (CKS), Certified Kubernetes Administrator (CKA), Certified Kubernetes Application Developer (CKAD), and Certified OpenStack Administrator (COA). He earned his Ph.D. studying design automation and enjoys all things tech.

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Lab steps
Signing In to the Google Cloud Console
Starting the Lab's Jupyter Notebook