Building AI-Powered Retail Search and Recommendations with Vertex AI Search for Commerce

Delivering personalized product search and recommendations is essential for modern retail success. In this course, join data scientist and machine learning engineer Amarachi Okpara as she shows you how to build AI-powered retail experiences using Vertex AI Search for Commerce, Google Cloud's scalable search and recommendation solution. Designed for cloud engineers, data scientists, and operations specialists, this course covers the essentials of data ingestion, model selection, personalized placements, performance monitoring, and more. Along the way, instructor-led demonstrations highlight core skills for configuring and deploying solutions that drive smarter ecommerce outcomes.

Learning objectives
Describe the end-to-end workflow of Vertex AI Search for Commerce, including architecture, key components, and data requirements.
Ingest and process retail catalog and user event data by identifying appropriate data sources and ingestion methods.
Differentiate the eight recommendation model types and determine their use cases across personalized search and product recommendation scenarios.
Implement personalized ecommerce experiences using placements, merchandising strategies, and attribution tokens to drive measurable sales outcomes.
Monitor system performance by interpreting key metrics, configuring alerts, and applying optimization best practices.