Course
digicode: VAISCO
Introduction to Vertex AI Search for Commerce
Course facts
- Understanding the core functionalities of Vertex AI Search for commerce
- Exploring use cases and solutions using Vertex AI Search for commerce
- Implementing data ingestion and quality pipelines for catalog and user event data
- Personalizing search results and recommendations for customers
- Monitoring search performance results
- Understanding advanced features and best practices for Vertex AI Search for commerce
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.
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
- 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
- Lab: Getting Started with Vertex AI Search for commerce
2 Data Ingestion
- Data ingestion pipelines
- Data sources (Cloud Storage, BigQuery, Merchant Center)
- Data transformations and pre-processing
- 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
- Lab: Performing data transformations and validation
3 Data Management
- More on data transformations and pre-processing
- Working with product metadata and attributes
- Data quality and consistent updates
- 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.
- Lab: Managing and updating product metadata
4 Search and Browse
- Data Quality
- Search and Browse Functionality Deep Dive
- Results Personalization
- Optimization Controls
- 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
- Lab: Personalizing Search Results with Vertex AI Search for commerce
5 Recommendations
- Recommendations Overview
- Recommendation Models
- Building a Recommendation Strategy
- Distinguish between different recommendation models
- Correlate page types with optimization objectives
- Build a strategy for implementing recommendations
Search Engineers, Data Engineers, and Data Scientists who wish to learn how to understand the core functionalities of Vertex AI Search for commerce.
We recommend having taken Modernizing Retail and Ecommerce Solutions with Google Cloud or equivalent knowledge.
Products:
- Vertex AI
- Vertex AI Search
- Gemini
- BigQuery
- Cloud Storage
- Dataflow