Course Overview of Introduction to Vertex AI Search for Commerce

This course provides a deep dive into Vertex AI Search for Commerce, a specialized solution designed to improve customer experiences through intelligent product discovery. You will learn to implement search apps, manage high-quality data ingestion for catalogs and user events, and leverage AI to personalize recommendations. The course covers everything from basic implementation to advanced features like query expansion, boosting, and performance monitoring. 

After completing Introduction to Vertex AI Search for Commerce, students will be able to:

  • Build and implement a functional search application for retail environments.
  • Manage and monitor complex data ingestion pipelines for product catalogs and user event data.
  • Optimize recommendation models using personalization and optimization objectives.
  • Use serving configurations and controls to deploy and test models.
  • Implement advanced search features like query expansion and dynamic faceting.
  • Analyze search performance and system health metrics to drive iterative improvements.

Upcoming Batches

Loading Dates...

Key Features of Introduction to Vertex AI Search for Commerce

  • Retail-Specific AI- Focuses on specialized search and recommendation models optimized for commerce objectives.

  • Hands-On Experience- Includes 7 comprehensive modules, 6 hands-on labs, and 6 classroom activities.

  • End-to-End Lifecycle- Covers data ingestion, model optimization, deployment, A/B testing, and health monitoring.

  • Advanced Search Controls- Learn to implement faceting, filtering, and result boosting to influence product ranking.

  • Performance Insights- Explore built-in analytics to monitor system health and business metrics.

Who should Attend Introduction to Vertex AI Search for Commerce?

  • Search Engineers
  • Data Engineers
  • Data Scientists focused on retail discovery and recommendation systems

Prerequisites of Introduction to Vertex AI Search for Commerce

  • Basic understanding of machine learning concepts and data engineering.
  • Familiarity with Google Cloud data storage and processing services.

    • Why choose CloudThat as your training partner for Introduction to Vertex AI Search for Commerce?

    • Specialized GCP Focus- CloudThat specializes in cloud technologies, offering focused and specialized training programs. We are Authorized Trainers for the Google Cloud Platform. This specialization ensures in-depth coverage of GCP services, use cases, best practices, and hands-on experience tailored specifically for GCP. 
    • Industry-Recognized Trainers- CloudThat has a strong pool of industry-recognized trainers certified by GCP. These trainers bring real-world experience and practical insights into the training sessions, comprehensively understanding how GCP is applied in different industries and scenarios. 
    • Hands-On Learning Approach- CloudThat emphasizes a hands-on learning approach. Learners can access practical labs, real-world projects, and case studies that simulate actual GCP environments. This approach allows learners to apply theoretical knowledge in practical scenarios, enhancing their understanding and skill set. 
    • Customized Learning Paths- CloudThat understands that learners have different levels of expertise and varied learning objectives. We offer customized learning paths, catering to beginners, intermediate learners, and professionals seeking advanced GCP skills. 
    • Interactive Learning Experience- CloudThat's training programs are designed to be interactive and engaging. We utilize various teaching methodologies like live sessions, group discussions, quizzes, and mentorship to keep learners engaged and motivated throughout the course. 
    • Placement Assistance and Career Support- CloudThat often provides placement assistance and career support services. This includes resume building, interview preparation, and connecting learners with job opportunities through our network of industry partners and companies looking for GCP-certified professionals. 
    • Continuous Learning and Updates- CloudThat ensures that our course content is regularly updated to reflect the latest trends, updates, and best practices within the GCP ecosystem. This commitment to keeping the content current enables learners to stay ahead in their GCP knowledge. 
    • Positive Reviews and Testimonials- Reviews and testimonials from past learners can strongly indicate the quality of training provided. You can Check feedback and reviews about our GCP courses that can provide potential learners with insights into the effectiveness and value of the training. 

    Learning Objectives Introduction to Vertex AI Search for Commerce

    • Core Functionality- Understand how Vertex AI Search for Commerce supports retail use cases.
    • Data Management- Implement ingestion and quality checks for catalog and event data.
    • Search Optimization- Apply personalization and boost controls to improve search recall and rankin
    • Deployment & Testing- Use A/B testing and experimentation to validate model deployment.
    • Advanced Features: Integrate the service with other Google Cloud services and use query expansion for better recall.

    Course Outline for Introduction to Vertex AI Search for Commerce Download Course Outline

    Lecture Content

    • Core functionalities of Vertex AI Search for commerce
    • Common retail use cases and automated e-commerce solutions
    • Overcoming customer discovery and conversion barriers

    Learning Objectives

    • Understand the core functionalities of Vertex AI Search for commerce
    • Explore commercial use cases and platform solutions

    Lecture Content

    • Planning and mapping a basic storefront search application
    • Constructing initial commerce search app engine structures
    • Basic API integration workflows

    Learning Objectives

    • Implement a basic retail search application in Vertex AI Search for commerce

    Lecture Content

    • Catalog data structural requirements, schema setups, and syncing
    • Managing real-time user event data streams
    • Building robust data quality pipelines to validate search index integrity

    Learning Objectives

    • Implement data ingestion and data quality pipelines for catalog and user event data

    Lab Content

    • Data Ingestion and Quality for Search and Recommendations

    Lecture Content

    • Activating intent-driven e-commerce personalization
    • Collecting, interpreting, and mapping ongoing user interaction signals
    • Aligning semantic results with customer contexts

    Learning Objectives

    • Personalize search results and user storefront interactions for customers

    Lab Content

    • Optimizing Search with Personalization and User Signals

    Lecture Content

    • Distinguishing between different machine-learned recommendation models
    • Correlating specific site page types with optimization objectives
    • Building an enterprise strategy for implementing Recommendations AI across storefronts

    Learning Objectives

    • Distinguish between different recommendation models
    • Correlate page types with optimization objectives
    • Build a strategy for implementing recommendations

    Lecture Content

    • Custom serving configurations and deployment control settings
    • Setting up A/B testing, experimentation variants, and user analytics
    • Monitoring application system health, latency, and click-through metrics
    • Continuous iterative optimization for retail search deployments

    Learning Objectives

    • Use serving configs and controls for search engine and model deployment
    • Validate deployments with live previews and monitor system health and metrics
    • Understand iterative optimization and performance tracking loops

    Lab Content

    • Implementing Recommendations AI Models and Configuring Retail Search

    Lecture Content

    • Query expansion rules to enhance low-match search recall
    • Implementing dynamic faceting and advanced filtering systems
    • Applying product ranking boost controls to steer promotional visibility
    • Integrating Vertex AI Search for commerce with peripheral Google Cloud services

    Learning Objectives

    • Use query expansion to improve search recall
    • Implement dynamic faceting to help end-users refine search results
    • Apply product ranking boost controls to systematically influence results
    • Integrate retail search components natively with other Google Cloud services

    Lab Content

    • Implementing Advanced Search Features

    Certification Details of Introduction to Vertex AI Search for Commerce

      CloudThat Course Completion Certificate will be awarded to all learners who complete the training.

    Click to Zoom

    Select Course date

    Loading Dates...
    Add to Wishlist

    Course ID: 29207

    Course Price at

    Loading price info...
    Enroll Now

    FAQs for Introduction to Vertex AI Search for Commerce

    It is a Google Cloud service that allows retailers to provide Google-quality search and recommendations on their own websites.

    Yes, Modules 05 and 06 focus specifically on recommendation models and implementation.

    Search is query-based, while recommendations are often based on user behavior and page types (e.g., "Others also bought").

    Yes, there are 6 hands-on labs covering ingestion, personalization, and advanced features.

    While common in data integration, here it refers to managing the quality of catalog and user event data.

    Yes, the course teaches A/B testing and experimentation for deployment.

    The search and recommendation engines can be integrated into both web and mobile platforms.

    Yes, Module 07 covers "Boosting Search Results" to influence rankings.

    It is a 2-day instructor-led session.

    Yes, Module 06 covers monitoring system health and analyzing search performance.

    Enquire Now