🏆SFEIR is the Google Cloud EMEA Training Partner of the Year 2025🤝New partnership: Official GitLab Training🤖New training: AI-Augmented Developer
🏆SFEIR is the Google Cloud EMEA Training Partner of the Year 2025🤝New partnership: Official GitLab Training🤖New training: AI-Augmented Developer
GCP200VAISCOM

Vertex AI Search for Commerce Training

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.

Google Cloud
✓ Official training Google CloudLevel Intermediate⏱️ 2 days (14h)

What you will 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.

Prerequisites

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

Target audience

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

Training Program

7 modules to master the fundamentals

Objectives
  • 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.
Topics covered
  • →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
Activities

Lab: Getting Started with Vertex AI Search for commerce

Objectives
  • 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.
Topics covered
  • →Data ingestion pipelines
  • →Data sources (Cloud Storage, BigQuery, Merchant Center)
  • →Data transformations and pre-processing
Activities

Lab: Performing data transformations and validation

Objectives
  • 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.
Topics covered
  • →More on data transformations and pre-processing
  • →Working with product metadata and attributes
  • →Data quality and consistent updates
Activities

Lab: Managing and updating product metadata

Objectives
  • 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.
Topics covered
  • →Data Quality
  • →Search and Browse Functionality Deep Dive
  • →Results Personalization
  • →Optimization Controls
Activities

Lab: Personalizing Search Results with Vertex AI Search for commerce

Objectives
  • Distinguish between different recommendation models.
  • Correlate page types with optimization objectives.
  • Build a strategy for implementing recommendations.
Topics covered
  • →Recommendations Overview
  • →Recommendation Models
  • →Building a Recommendation Strategy
Objectives
  • 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.
Topics covered
  • →Serving Configurations and Controls
  • →A/B Testing and Experimentation
  • →Analytics
  • →Monitoring
Activities

Lab: Implementing Recommendations AI Models and Configuring Retail Search

Objectives
  • 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.
Topics covered
  • →Query Expansion
  • →Faceting and Filtering
  • →Boosting Search Results
  • →Vertex AI Search for commerce Integration with other Google Cloud Services
Activities

Lab: Implementing Advanced Search Features

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Quality Process

SFEIR Institute's commitment: an excellence approach to ensure the quality and success of all our training programs. Learn more about our quality approach

Teaching Methods Used
  • Lectures / Theoretical Slides — Presentation of concepts using visual aids (PowerPoint, PDF).
  • Technical Demonstration (Demos) — The instructor performs a task or procedure while students observe.
  • Guided Labs — Guided practical exercises on software, hardware, or technical environments.
  • Quiz / MCQ — Quick knowledge check (paper-based or digital via tools like Kahoot/Klaxoon).
Evaluation and Monitoring System

The achievement of training objectives is evaluated at multiple levels to ensure quality:

  • Continuous Knowledge Assessment : Verification of knowledge throughout the training via participatory methods (quizzes, practical exercises, case studies) under instructor supervision.
  • Progress Measurement : Comparative self-assessment system including an initial diagnostic to determine the starting level, followed by a final evaluation to validate skills development.
  • Quality Evaluation : End-of-session satisfaction questionnaire to measure the relevance and effectiveness of the training as perceived by participants.

Frequently Asked Questions

Developers, data and e-commerce teams who want to deliver high-quality search, browse and recommendation experiences for retail and commerce sites on Google Cloud.
Basic familiarity with Google Cloud is recommended. No prior experience with Vertex AI Search is required.
You will be able to implement a search app, manage catalog and user-event data ingestion and quality, and personalize search results and recommendations for your customers.
The course focuses on commerce and retail use cases: product search, browse, recommendations and personalization to improve conversion and customer experience.
The training lasts 2 days and alternates theory, live demonstrations and hands-on labs on Google Cloud.
Our training organizations SFEIR SAS and SFEIR-Est are Qualiopi certified for training activities. Contact us for a quote.

1,580€ excl. VAT

per learner

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