Skip to main content
T-VAISCOM-IOfficial Google Curriculum

Introduction to Vertex AI Search for Commerce

2 daysILTIntermediateLoading...

Overview

In this course you will explore Vertex AI Search for commerce and how it can be used to improve customer experience. You will explore the core functionalities of Vertex AI Search for commerce with a discussion on common use cases and solutions before implementing a basic search app in Vertex AI Search for commerce. Afterwards, you will discuss how to manage data ingestion and quality for your search app, optimize recommendations with personalization, deploy your search app, monitor and analyze search performance, and discuss advanced features and general best practices.

What You'll Learn

  • Understand the core functionalities of Vertex AI Search for commerce
  • Explore use cases and solutions using Vertex AI Search for commerce
  • Implement data ingestion and quality pipelines for catalog and user event data
  • Personalize search results and recommendations for customers
  • Monitor search performance results
  • Understand advanced features and best practices for Vertex AI Search for commerce

Who Should Attend

Search Engineers, Data Engineers, and Data Scientists who wish to learn how to understand the core functionalities of Vertex AI Search for commerce.

Prerequisites

"Modernizing Retail and Ecommerce Solutions with Google Cloud" or equivalent experience with Google Cloud

Products Covered

Vertex AIVertex AI SearchGeminiBigQueryCloud StorageDataflow

Course Modules

1

Introduction to Vertex AI Search for Commerce

Topics

  • Overview of Vertex AI Search for commerce
  • Key concepts for Vertex AI Search for commerce
  • Tour of Vertex AI Search for commerce in the Cloud Console
  • Example use cases

Learning Outcomes

  • Understand key concepts for Vertex AI Search for commerce
  • Leverage Vertex AI Search for commerce features and capabilities
  • Discover typical use cases for Vertex AI Search for commerce

Activities

Lab: Getting Started with Vertex AI Search for commerce
2

Data Ingestion

Topics

  • Data ingestion pipelines
  • Data sources (Cloud Storage, BigQuery, Merchant Center)
  • Data transformations and pre-processing

Learning Outcomes

  • Ingest product data into Vertex AI Search for commerce using ETL pipelines
  • Track user events in real time
  • Manage ongoing updates to keep data fresh

Activities

Lab: Performing data transformations and validation
3

Data Management

Topics

  • More on data transformations and pre-processing
  • Working with product metadata and attributes
  • Data quality and consistent updates

Learning Outcomes

  • Understand key product data structures for Vertex AI
  • Identify essential attributes and their impact on AI performance
  • Explore advanced data transformation techniques for catalogs
  • Align product data with Google Cloud Retail schema for optimal results

Activities

Lab: Managing and updating product metadata
4

Search and Browse

Topics

  • Data Quality
  • Search and Browse Functionality Deep Dive
  • Results Personalization
  • Optimization Controls

Learning Outcomes

  • Distinguish search vs. browse functionalities
  • Understand search and browse performance tiers
  • Improve and maintain data quality
  • Describe ranking, optimization, and personalization
  • Identify key catalog and user event attributes

Activities

Lab: Personalizing Search Results with Vertex AI Search for commerce
5

Recommendations

Topics

  • Recommendations Overview
  • Recommendation Models
  • Building a Recommendation Strategy

Learning Outcomes

  • Distinguish between different recommendation models
  • Correlate page types with optimization objectives
  • Build a strategy for implementing recommendations
6

Deployment, Monitoring, and Testing

Topics

  • Serving Configurations and Controls
  • A/B Testing and Experimentation
  • Analytics
  • Monitoring

Learning Outcomes

  • Use serving configs and controls for model deployment
  • Validate deployment with previews
  • Monitor system health and metrics
  • Understand iterative optimization for Vertex AI Search for commerce

Activities

Lab: Implementing Recommendations AI Models and Configuring Retail Search
7

Advanced Features

Topics

  • Query Expansion
  • Faceting and Filtering
  • Boosting Search Results
  • Vertex AI Search for commerce Integration with other Google Cloud Services

Learning Outcomes

  • Use query expansion to improve search recall
  • Implement dynamic faceting to help users refine results
  • Apply boost controls to influence product ranking
  • Integrate Vertex AI Search for commerce with other Google Cloud services

Activities

Lab: Implementing Advanced Search Features

Get This Training

No public classes currently scheduled. Express interest below or request private training.

Request Private Session

Course Details

Course Code
T-VAISCOM-I
Duration
2 days
Format
ILT
Level
Intermediate
Modules
7
Activities
12
Price
Loading...
View Official Google Datasheet →

Questions About This Course?

Contact us for custom scheduling, group discounts, or curriculum customization.

Contact Us
Starting fromLoading...