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Vertex AI Search for Retail Agent Builder

Google Cloud Training

This on-demand course provides partners the skills required to design, deploy, and monitor Vertex AI Search for Retail Agent Builder solutions including retail search and recommendation AI for enterprise customers. Retail Search was previously known as Discovery AI so you may see references to the older product name in this course.

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What's inside

Syllabus

Introduction
This introductory module outlines what this course will cover and provides an overview of the Discovery AI solution and its benefits.
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Develops professional skills used by Retail Search and Recommendation Engineers for enterprise customers
Offered by Google Cloud Training, reputable in its domain
Prerequisites required, which may present difficulties for some

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Reviews summary

Implementing vertex ai retail solutions

According to the course design, learners are expected to gain skills in designing, deploying, and monitoring Vertex AI Search for Retail Agent Builder solutions. This course aims to provide a structured understanding of retail search and recommendation AI, covering topics from architecture overview and data ingestion to model training and serving configurations. It is designed for professionals implementing enterprise AI solutions. Potential learners should be prepared for a technically focused curriculum that may presume some prior familiarity with AI concepts and Google Cloud.
Equips learners with deployable, real-world skills for retail AI.
"This course helped me understand how to actually integrate AI models into a live website using serving configurations."
"The focus on enterprise customers means the skills are directly applicable to my professional work in retail."
"I found the sections on monitoring and management particularly useful for ongoing system operations."
Covers the full lifecycle of Vertex AI Search for Retail solutions.
"I appreciate how the course goes from understanding the core architecture to the practical aspects of data ingestion and model deployment."
"The content spans everything needed to implement Retail Search and Recommendations, from initial concept to ongoing operations."
"It provides a clear roadmap for designing and deploying AI solutions specifically in a retail environment."
Could benefit from more extensive or detailed practical lab exercises.
"While theoretical concepts are well-explained, I wish there were more guided labs to reinforce the learning."
"I had to supplement the course with my own experiments to fully grasp the implementation details."
"More hands-on demos, especially for complex integration scenarios, would enhance the learning process significantly."
Requires existing familiarity with AI/ML and Google Cloud concepts.
"As an intermediate learner, I felt the pace was good, but a beginner might struggle without prior cloud or ML exposure."
"The course jumps straight into advanced topics, so having a foundational understanding beforehand is crucial."
"I'd recommend reviewing core Google Cloud AI services before diving into this specific agent builder."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Vertex AI Search for Retail Agent Builder with these activities:
Review HTTP Concepts
Reviewing HTTP Concepts can help ensure that learners have a solid foundation in the fundamental concepts of the web, preparing them to succeed in this course.
Browse courses on HTTP
Show steps
  • Read resources on HTTP concepts
  • Practice using HTTP commands
Review previous experience with data management
Refresh your memory on fundamental data management concepts to prepare for this course and its projects.
Browse courses on Data Management
Show steps
  • Review data management concepts
  • Refresh your knowledge of data structures
  • Practice data manipulation techniques
Review basic concepts of machine learning and artificial intelligence
Strengthens foundational knowledge in machine learning and AI, which are essential for understanding Vertex AI Search for Retail Agent Builder.
Browse courses on Machine Learning
Show steps
  • Review online tutorials or articles on machine learning concepts
  • Complete practice problems or exercises
  • Discuss concepts with peers or mentors
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Study Group Discussions
Participating in Study Group Discussions encourages learners to engage with their peers, exchange ideas, and reinforce their understanding through peer-to-peer learning.
Show steps
  • Form a study group
  • Meet regularly and discuss course topics
Explore the Vertex AI Search for Retail Agent Builder documentation
Provides a thorough understanding of Vertex AI Search for Retail Agent Builder by exploring its comprehensive documentation.
Show steps
  • Visit the Vertex AI Search for Retail Agent Builder documentation website
  • Review the introductory materials to get an overview of the platform
  • Explore the specific sections related to your interests or learning needs
Join a study group or discussion forum for Vertex AI Search for Retail Agent Builder
Enhances understanding of Vertex AI Search for Retail Agent Builder by engaging in discussions and sharing knowledge with peers.
Show steps
  • Identify a study group or discussion forum focused on Vertex AI Search for Retail Agent Builder
  • Join the group and actively participate in discussions
  • Share your insights and experiences with others
Develop a use case for Vertex AI Search for Retail Agent Builder in your organization
Provides a practical application for Vertex AI Search for Retail Agent Builder by creating a use case specific to your organization.
Show steps
  • Identify a specific business problem or opportunity that Vertex AI Search for Retail Agent Builder could address
  • Develop a detailed use case document that outlines the problem, the solution, and the expected benefits
  • Present your use case to stakeholders and get buy-in
Build a Recommendation Model
Building a Recommendation Model allows learners to apply the theoretical concepts covered in the course to a practical scenario, solidifying their understanding.
Browse courses on Machine Learning Fundamentals
Show steps
  • Gather and prepare data
  • Choose and train a model
  • Evaluate and refine the model
Practice writing recommendations and configuring serving in Vertex AI Search for Retail Agent Builder
Reinforces understanding of how to write recommendations and configure serving in Vertex AI Search for Retail Agent Builder by practicing these tasks.
Show steps
  • Create a small dataset of retail products
  • Write a recommendation model using available training data
  • Configure serving to integrate your model into a website or application
  • Evaluate your model's performance
Participate in a hackathon or competition that involves Vertex AI Search for Retail Agent Builder
Provides a challenging and engaging environment to apply and test knowledge of Vertex AI Search for Retail Agent Builder.
Show steps
  • Identify a hackathon or competition that aligns with your interests
  • Team up with other participants or work independently
  • Develop a solution that leverages Vertex AI Search for Retail Agent Builder
  • Submit your solution and compete for recognition
Organize and summarize the key concepts covered in the Vertex AI Search for Retail Agent Builder course
Reinforces understanding of Vertex AI Search for Retail Agent Builder by organizing and summarizing the key concepts covered in the course.
Show steps
  • Review the course materials and identify the main topics
  • Create a summary or outline of the key concepts

Career center

Learners who complete Vertex AI Search for Retail Agent Builder will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 43 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Vertex AI Search for Retail Agent Builder.
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Mathematics for Machine Learning
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Kafka: The Definitive Guide
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Introduction to Machine Learning with Python
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Instructs data scientists on applied model evaluation, cross-validation, hyperparameter tuning, and pipeline construction through Scikit-Learn maintainers Andreas Müller and Sarah Guido.
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MLOps Engineering at Scale
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Focusing on scaling MLOps practices, this book is for professionals dealing with large-scale ML deployments. It covers strategies and best practices for building and managing robust and scalable model training and deployment pipelines. It's highly relevant for engineers and architects in an enterprise setting.
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Designing Data-Intensive Applications
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While not solely focused on data ingestion, this book provides a deep dive into the fundamental concepts of data systems, including how data is stored, processed, and moved. Understanding these underlying principles is crucial for anyone working with data ingestion pipelines. It's considered a must-read classic for data professionals and offers valuable insights into the trade-offs and challenges involved in building data-intensive applications.
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Kafka Streams in Action
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A practical guide to building real-time applications and microservices using the Kafka Streams API. is highly relevant for implementing streaming data ingestion and processing directly within the Kafka ecosystem. It offers hands-on examples and covers testing and operational aspects.
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Machine Learning in Production
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Investigates the engineering realities of running automated ML pipelines in production, addressing data drift detection, automated retraining loops, telemetry infrastructure, and continuous validation.
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Spark: The Definitive Guide
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Apache Spark powerful engine for big data processing, including batch and stream processing which are often part of data ingestion workflows. Written by one of Spark's creators, this book provides a comprehensive guide to using Spark for various data tasks. It's a valuable resource for understanding how Spark can be leveraged for scalable data ingestion and processing.
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Applied Predictive Modeling
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Build a Large Language Model (from Scratch)
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Jeff Lowe
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Presents reusable design patterns for common data engineering challenges, including those related to data ingestion. It offers proven solutions and best practices for building reliable and efficient data pipelines. It's a valuable resource for learning established approaches to data ingestion problems.
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Delves into the complexities of designing and building large-scale streaming data systems. It's highly relevant for understanding real-time data ingestion and processing patterns. It's a more advanced book, suitable for those looking to deepen their knowledge of streaming architectures.
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Data Engineering with Python
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Provides a practical approach to data engineering using Python, a widely used language for building data ingestion pipelines. It covers designing data models and automating pipelines, offering hands-on knowledge for implementing ingestion solutions. It's a good resource for those who want to apply their programming skills to data engineering tasks.
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Focuses on building data pipelines specifically on Amazon Web Services (AWS), a major cloud provider. It covers relevant AWS services for data ingestion and transformation, making it a practical guide for those working within the AWS ecosystem. It's a good resource for understanding cloud-specific data ingestion patterns.
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Data Engineering with Google Cloud Platform
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Similar to the AWS book, this guide focuses on building data pipelines on Google Cloud Platform (GCP). It covers GCP services relevant to data ingestion, warehousing, and workflow orchestration. It's a valuable resource for those utilizing the GCP ecosystem for their data ingestion needs.
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Data Mesh in Action
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Provides a practical guide to implementing the Data Mesh concept. It complements 'Data Mesh: Delivering Data-Driven Value at Scale' by offering actionable steps and examples for adopting a data mesh architecture, which directly impacts data ingestion patterns and ownership.
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Natural Language Processing with Python
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Introduces natural language processing concepts and techniques using Python's NLTK library. It covers various models and approaches used for training models on text data. It's a foundational text for those interested in applying machine learning to NLP tasks. The principles of training models on sequential data are particularly relevant.
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Designing Cloud Data Platforms
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Focuses on the design of data platforms in the cloud, which are common environments for modern data ingestion. It covers architectural considerations and best practices for building scalable and robust data platforms in the cloud, offering valuable context for cloud-based data ingestion.
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Data Management at Scale
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Explores modern data architecture patterns like Data Mesh and Data Fabric, which influence how data is managed and accessed at scale. Understanding these architectures provides context for designing effective data ingestion strategies in complex environments. It offers a broader perspective on data management beyond just ingestion.
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Bridges the gap between statistics and data science, explaining essential statistical concepts used in model training. It provides practical guidance with code examples in R and Python, making it useful for data scientists and analysts involved in building and evaluating models. It helps solidify the statistical understanding behind model training.
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Database Internals
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Understanding the internals of databases and distributed data systems is beneficial for comprehending how ingested data is stored and managed. provides a detailed look at the mechanisms within these systems, offering valuable context for data ingestion professionals. It's a technically deep book suitable for those with a strong interest in database architecture.
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Database System Concepts
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A foundational textbook on database systems, this book covers essential concepts related to data storage, management, and querying. A solid understanding of database concepts is beneficial for anyone working with data ingestion, as databases are often sources or destinations for ingested data. It's a comprehensive reference for database fundamentals.
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ChatGPT Profits
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Provides a collection of recipes for using Vertex AI. It covers a wide range of topics, from data preprocessing to model deployment. This book is especially valuable for beginners who want to get started with Vertex AI.
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Distributed Systems
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This classic textbook provides a foundational understanding of distributed systems, which are the backbone of most modern data ingestion and processing platforms. While not specific to data ingestion, the principles covered are essential for designing and troubleshooting distributed data pipelines. It's a valuable resource for building a strong theoretical foundation.
Distributed Systems
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Distributed Systems
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Naked Statistics: Stripping the Dread from the Data
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While not solely focused on machine learning, this book provides an accessible and intuitive introduction to statistical concepts that are fundamental to understanding model training. It explains key ideas like correlation, regression, and inference without heavy mathematics, making it excellent for building prerequisite knowledge. It's a great starting point for those with limited statistical background.
Naked Statistics: Stripping the Dread from the Data
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Professional Microsoft SQL Server 2012 Analysis...
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Provides a practical guide to building a data warehouse using SQL Server, covering ETL processes which include data ingestion. While tied to a specific technology, the ETL principles discussed are broadly applicable. It's a good resource for understanding the practicalities of loading data into a data warehouse.
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Principles of Data Mining
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Understanding the goals of data analysis and mining can inform data ingestion strategies. covers the principles of data mining, providing context for why data is ingested and how it will be used downstream. It's more focused on the 'why' behind data ingestion rather than the 'how'.
Principles of Data Mining (Adaptive Computation and...
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Effort
5 hours to complete
Level
Advanced
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Develops professional skills used by Retail Search and Recommendation Engineers for enterprise customers
Offered by Google Cloud Training, reputable in its domain
Prerequisites required, which may present difficulties for some
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