Introduction to Vertex AI Search for Commerce
(2 days)
Course Description
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.
Learning Objectives
- 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
Course Outline
Module 1: Introduction to Vertex AI Search for Commerce
- 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
- Lab: Getting Started with Vertex AI Search for commerce
Module 2: Data Ingestion
- Data ingestion pipelines
- Data sources (Cloud Storage, BigQuery, Merchant Center)
- Data transformations and pre-processing
- Lab: Performing data transformations and validation
Module 3: Data Management
- More on data transformations and pre-processing
- Working with product metadata and attributes
- Data quality and consistent updates
- Lab: Managing and updating product metadata
Module 4: Search and Browse
- Data Quality
- Search and Browse Functionality Deep Dive
- Results Personalization
- Optimization Controls
- Lab: Personalizing Search Results with Vertex AI Search for commerce
Module 5: Recommendations
- Recommendations Overview
- Recommendation Models
- Building a Recommendation Strategy
Module 6: Deployment, Monitoring, and Testing
- Serving Configurations and Controls
- A/B Testing and Experimentation
- Analytics
- Monitoring
- Lab: Implementing Recommendations AI Models and Configuring Retail Search
Module 7: Advanced Features
- Query Expansion
- Faceting and Filtering
- Boosting Search Results
- Vertex AI Search for commerce Integration with other Google Cloud Services
- Lab: Implementing Advanced Search Features
