Detailed Course Outline
Module 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
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
Activities:
- Lab: Getting Started with Vertex AI for commerce
Module 2 - Data Ingestion
Topics:
- Data ingestion pipelines
- Data sources (Cloud Storage, BigQuery, Merchant Center)
- Data transformations and pre-processing
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
Activities:
- Lab: Performing data transformations and validation
Module 3 - Data Management
Topics:
- More on data transformations and pre-processing
- Working with product metadata and attributes
- Data quality and consistent updates
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.
Activities:
- Lab: Managing and updating product metadata
Module 4 - Search and Browse
Topics:
- Data Quality
- Search and Browse Functionality Deep Dive
- Results Personalization
- Optimization Controls
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
Activities:
- Lab: Personalizing Search Results with Vertex AI Search for commerce
Module 5 - Recommendations
Topics:
- Recommendations Overview
- Recommendation Models
- Building a Recommendation Strategy
Objectives:
- Distinguish between different recommendation models
- Correlate page types with optimization objectives
- Build a strategy for implementing recommendations
Module 6 - Deployment, Monitoring, and Testing
Topics:
- Serving Configurations and Controls
- A/B Testing and Experimentation
- Analytics
- Monitoring
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
Activities:
- Lab: Implementing Recommendations AI Models and Configuring Retail Search
Module 7 - Advanced Features
Topics:
- Query Expansion
- Faceting and Filtering
- Boosting Search Results
- Vertex AI Search for commerce Integration with other Google Cloud Services
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
Activities:
- Lab: Implementing Advanced Search Features