We may earn an affiliate commission when you visit our partners.
Google Cloud Training
This is a self-paced lab that takes place in the Google Cloud console. In this lab, you will learn how to build a question answering system that understands both text and images with Vertex AI.
Register for this course and see more details by visiting:
OpenCourser.com/course/sd8h0r/build
Here's a deal for you
Save money when you learn with a deal that may be relevant to this course.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
Get offer
What's inside
Syllabus
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Uses Vertex AI, which is a managed platform for deploying machine learning models, making it suitable for those looking to streamline their AI workflows
Offered by Google Cloud, which is recognized for its contributions to cloud computing and artificial intelligence technologies
Focuses on building a question answering system, which is a practical application of AI with relevance to information retrieval and customer service
Requires access to the Google Cloud console, which may involve a subscription or payment for usage beyond the free tier
Save this course
Create your own learning path.
Save this course to your list so you can find it easily later.
Save
Save
Reviews summary
Hands-on vertex ai multimodal q&a lab
According to students, this course is a practical, hands-on lab (positive) focused on building a multimodal question answering system using Google Cloud's Vertex AI (neutral). Learners find the steps provided are clear and easy to follow (positive), making it a great introduction to Vertex AI for beginners (positive) or those new to multimodal systems. The hands-on activities (positive) are particularly praised for providing valuable practical experience (positive). While the course offers a solid foundation (positive), some learners suggest it could benefit from more in-depth explanations of the underlying concepts (warning) and potentially additional scenarios or advanced topics (neutral). Overall, it is seen as a highly useful and well-structured lab (positive) for its specific goal.
Covers a specific application of multimodal AI.
"This lab is specifically for building a DIY Multimodal Question Answering System, which is a niche but interesting application."
"It tackles a very specific problem, which is great if that's what you need, but not a broad AI overview."
"Focuses intently on the multimodal QA architecture within Vertex AI."
"The course is targeted towards this particular system build rather than general Vertex AI use."
Serves as a solid starting point for Vertex AI.
"A good introduction to building a multimodal QA system and using Vertex AI."
"As a beginner, this lab helped me get started with Vertex AI and its capabilities."
"It gave me a basic understanding of how to leverage Google Cloud for AI projects."
"This course is excellent for anyone looking for their first hands-on project with Vertex AI."
Steps are well-explained and easy to follow.
"The steps were easy to follow for setting up the multimodal question answering system."
"This lab was well-structured and made it simple to follow along even with complex technologies."
"I found the instructions very clear, which made implementing the system straightforward."
"The course breaks down the process into manageable, easy-to-understand steps."
Course offers valuable hands-on experience.
"The most important part of this lab is that it gives practical experience of Google cloud services."
"It is a hands-on lab that helps you understand the flow of data and configuration for building a multimodal QA system."
"This course was a great hands-on experience into using Vertex AI for a unique use-case."
"I really appreciated the practical lab environment to build this system."
Focus is on steps, less on theory.
"The course is heavily focused on the steps and execution but lacks deeper explanation of the underlying concepts."
"I wish there was more theory behind *why* we are doing certain steps, not just *how*."
"Could be improved with more detailed explanations of the AI models being used."
"It assumes some prior knowledge of concepts and primarily guides through the technical implementation."
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 Build a DIY Multimodal Question Answering System with Vertex AI with these
activities:
Review Foundational Concepts of Question Answering Systems
Show steps
Reviewing the basics of question answering systems will provide a solid foundation for understanding the more advanced multimodal aspects covered in the course.
Show steps
-
Read articles on question answering architectures.
-
Summarize the key components of a QA system.
-
Identify different types of question answering tasks.
Brush up on Vertex AI Fundamentals
Show steps
Familiarizing yourself with Vertex AI will streamline the lab experience and allow you to focus on the multimodal question answering aspects.
Browse courses on
Vertex AI
Show steps
-
Complete the Vertex AI quickstart guide.
-
Explore the Vertex AI documentation.
-
Practice deploying a simple model on Vertex AI.
Follow a Text-Based Question Answering Tutorial on Vertex AI
Show steps
Working through a text-based question answering tutorial on Vertex AI will provide practical experience and build confidence before tackling the multimodal system.
Browse courses on
Vertex AI
Show steps
-
Find a suitable tutorial online.
-
Implement the tutorial step-by-step.
-
Modify the tutorial to use your own dataset.
Three other activities
Expand to see all activities and additional details
Show all six activities
Document Your Multimodal QA System Build
Show steps
Creating detailed documentation of your system will reinforce your understanding and provide a valuable resource for future reference.
Show steps
-
Describe the system architecture.
-
Explain the data preprocessing steps.
-
Document the model training process.
-
Provide instructions for deploying the system.
Expand the System with a New Data Source
Show steps
Extending the system with a new data source will challenge you to apply your knowledge and explore the limitations of the current architecture.
Show steps
-
Identify a relevant new data source.
-
Preprocess the new data to match the existing format.
-
Integrate the new data into the system.
-
Evaluate the performance of the expanded system.
Contribute to an Open Source Multimodal Project
Show steps
Contributing to an open-source project will provide valuable experience in collaborative development and expose you to different approaches to multimodal question answering.
Show steps
-
Find an open-source project related to multimodal QA.
-
Identify a bug or feature to work on.
-
Submit a pull request with your changes.
-
Respond to feedback from the project maintainers.
Review Foundational Concepts of Question Answering Systems
Show steps
Reviewing the basics of question answering systems will provide a solid foundation for understanding the more advanced multimodal aspects covered in the course.
Show steps
Show steps
Show steps
- Read articles on question answering architectures.
- Summarize the key components of a QA system.
- Identify different types of question answering tasks.
Brush up on Vertex AI Fundamentals
Show steps
Familiarizing yourself with Vertex AI will streamline the lab experience and allow you to focus on the multimodal question answering aspects.
Browse courses on
Vertex AI
Show steps
Show steps
Show steps
- Complete the Vertex AI quickstart guide.
- Explore the Vertex AI documentation.
- Practice deploying a simple model on Vertex AI.
Follow a Text-Based Question Answering Tutorial on Vertex AI
Show steps
Working through a text-based question answering tutorial on Vertex AI will provide practical experience and build confidence before tackling the multimodal system.
Browse courses on
Vertex AI
Show steps
Show steps
Show steps
- Find a suitable tutorial online.
- Implement the tutorial step-by-step.
- Modify the tutorial to use your own dataset.
Three other activities
Expand to see all activities and additional details
Show all six activities
Document Your Multimodal QA System Build
Show steps
Creating detailed documentation of your system will reinforce your understanding and provide a valuable resource for future reference.
Show steps
Show steps
Show steps
- Describe the system architecture.
- Explain the data preprocessing steps.
- Document the model training process.
- Provide instructions for deploying the system.
Expand the System with a New Data Source
Show steps
Extending the system with a new data source will challenge you to apply your knowledge and explore the limitations of the current architecture.
Show steps
Show steps
Show steps
- Identify a relevant new data source.
- Preprocess the new data to match the existing format.
- Integrate the new data into the system.
- Evaluate the performance of the expanded system.
Contribute to an Open Source Multimodal Project
Show steps
Contributing to an open-source project will provide valuable experience in collaborative development and expose you to different approaches to multimodal question answering.
Show steps
Show steps
Show steps
- Find an open-source project related to multimodal QA.
- Identify a bug or feature to work on.
- Submit a pull request with your changes.
- Respond to feedback from the project maintainers.
Career center
Learners who complete Build a DIY Multimodal Question Answering System with Vertex AI will develop knowledge and skills
that may be useful to these careers:
Deep Learning Engineer
Deep Learning Engineer
A deep learning engineer designs, builds, and trains deep learning models, and this course helps build a practical foundation in the field. This role often requires expertise in neural networks and other advanced algorithms. The practical work of building a multimodal question answering system with Vertex AI helps build skills in deep learning model development. This course may be useful to gain hands-on experience training and deploying models.
Computer Vision Engineer
Computer Vision Engineer
A computer vision engineer focuses on the development of systems that can interpret and understand images. This course helps one build a foundation for working with images via machine learning. Such an engineer is focused on how computers understand visual data, often applying machine learning. This course, with an emphasis on image processing in combination with text, fits well with an important subfield of computer vision. This course may be helpful for engineers tasked with building systems that integrate multiple types of data.
Natural Language Processing Engineer
Natural Language Processing Engineer
A natural language processing engineer focuses on building systems that understand and process human language, and this course provides a relevant project. This course may be useful for those focused on creating systems that can understand and respond to questions, a core area of NLP. This role applies algorithms and models to analyze text for various applications. The question answering system built in the course can help individuals build an understanding of how to develop NLP models. This course may be useful for those who want hands-on experience.
Machine Learning Engineer
Machine Learning Engineer
A machine learning engineer is responsible for building and deploying machine learning models, and this course provides relevant hands-on experience. This individual designs and implements machine learning systems for various applications, ensuring their performance and scalability. Specifically, building a multimodal question answering system helps build a foundation for developing other complex models. The work includes data preparation, model training, and deployment, all crucial steps for machine learning engineers. This course may be useful to building an understanding of practical application of multimodal models.
Artificial Intelligence Specialist
Artificial Intelligence Specialist
An artificial intelligence specialist focuses on the research, development, and implementation of AI solutions, and this course focuses on a key AI subfield. This specialist may design and implement AI algorithms, often working directly with large datasets and working towards specific AI project goals. The course’s focus on building a question answering system with text and image understanding components helps build a basic understanding of AI systems and how they are constructed. The course may be useful to understand the nuances of machine learning and model building.
Software Developer
Software Developer
A software developer writes and tests code, and this course may be helpful as a practical education in the machine learning side of software development. They work with various programming languages to create software applications. This course in machine learning may help software developers in building other complex systems. Building a question answering system helps build a foundation in the machine learning aspect of software development. This course may be useful in understanding the practical steps involved in deploying AI systems.
Cloud Solutions Architect
Cloud Solutions Architect
A cloud solutions architect designs and implements cloud based solutions, and this course provides a hands-on introduction to AI on the cloud. This professional works on ensuring that cloud solutions meet the organization's needs, working with various cloud services and tools. Building a question answering system on Google Cloud provides direct experience with a specific cloud platform. This course may be useful in building an understanding of AI deployment on the cloud.
Image Analyst
Image Analyst
An image analyst uses techniques to extract insights and information from digital images. This course may be useful for those seeking to explore machine learning for image analysis. An image analyst may work in fields such as medicine, remote sensing, and surveillance, often using computer vision methods. The course’s focus on image data as a component of machine learning may be useful for this role. This course may provide practical experience with working with image and text data.
Data Scientist
Data Scientist
A data scientist is responsible for analyzing complex data, generating insights, and developing data driven solutions, and this course may help introduce model creation for data analysis. Data scientists work with a variety of tools and techniques to extract information from data. This course may be useful as a practical exposure to building a machine learning model. Specifically, the course’s focus on question answering systems can help build a foundation for developing other types of models. This course may be helpful for those looking to explore applied machine learning.
AI Product Manager
AI Product Manager
An AI product manager oversees the development and launch of AI powered products, requiring an understanding of AI systems, and this course may help build that understanding. This role works closely with engineering teams and stakeholders to ensure that the AI product meets both business and user needs. Building a question answering system from start to finish helps build a basic understanding of how these systems are constructed and deployed. This course may be helpful for those who need a practical understanding of AI systems.
AI Consultant
AI Consultant
An AI consultant provides expert guidance to organizations on implementing AI solutions, and this course may help build practical knowledge of AI systems. They analyze clients' needs and recommend AI strategies and solutions. A practical understanding of how AI systems are built is important to advise clients. The hands-on experience of building a complete question answering system may be useful for consultants. This course may be helpful for building a practical perspective on AI development.
Robotics Engineer
Robotics Engineer
A robotics engineer designs, develops, and tests robots, and this course may be helpful for building an understanding of machine learning for robotics. This role often involves working with multiple data inputs such as images and text. Building a multimodal question answering system is relevant to robotics where both visual and textual data may be important. This course may be useful to understand how to incorporate multiple data sources into a machine learning model.
Machine Learning Researcher
Machine Learning Researcher
A machine learning researcher develops theory, algorithms, and models to advance the field of machine learning, and this course may provide relevant practical skills. This individual often has a strong background in mathematics and statistics, and often pursues novel solutions. Although an advanced degree is usually required for this role, this course may be helpful as a practical exposure to current machine learning techniques. Specifically, the course's focus on multimodal data may be useful to understand the challenges of combining visual data with textual data.
Data Analyst
Data Analyst
A data analyst interprets data in order to provide insights, and this course may be useful for expanding their data toolkit. Data analysts analyze and present data, often using tools and dashboards. While not directly related to the core tasks of a data analyst, the course may be helpful for an analyst to understand the basics of machine learning. Building a question answering system may be useful to build a basic familiarity with machine learning. This course may be useful for those looking to expand their data skill set.
Research Scientist
Research Scientist
A research scientist explores new scientific questions and develops innovative solutions, and this course may be a good introduction to the practical side of AI research. Research scientists often work on cutting-edge technologies and conduct experiments to improve existing solutions or create new ones. This course may be useful to understand the current state of multimodal machine learning. Although an advanced degree is usually required for this role, this course may be helpful as a hands-on practical training.
For more career information including salaries, visit:
OpenCourser.com/course/sd8h0r/build
Reading list
We haven't picked any books for this reading list yet.
Provides a comprehensive overview of machine learning on Google Cloud Platform, including Vertex AI. It covers the fundamentals of machine learning, as well as how to build, train, and deploy models using Vertex AI.
Save
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.
Is an introduction to GCP for beginners.
Save
Provides a comprehensive overview of GCP, with multiple sections devoted completely to Google Cloud Console.
Practical guide to GCP for beginners, with a chapter on Google Cloud Console.
Is an accessible introduction to GCP, with a section on Google Cloud Console.
Provides a hands-on approach to learning GCP, with some coverage of Google Cloud Console.
Study guide for the Google Cloud Certified Professional Cloud Architect exam, and it covers Google Cloud Console.
Provides a broad overview of Google Cloud Platform, covering various services accessible through the Cloud Console. It's suitable for gaining a foundational understanding of GCP and how its components interact. While published in 2018, it remains a valuable resource for grasping core concepts before diving into more recent or specialized topics. It can serve as a useful reference for understanding the breadth of services available.
Published in 2023, this guide offers a comprehensive look at a wide range of GCP services, including computing, storage, database, and networking. It's designed to familiarize readers with the various services accessible via the Cloud Console and provides an overview of topics like Big Data services and APIs. is highly relevant for gaining a broad understanding of the platform and its console interface.
While a study guide for a specific certification, this book offers a strong introduction to the core GCP services and concepts frequently accessed through the Cloud Console. It covers essential areas like computing, storage, networking, and security. is excellent for solidifying foundational knowledge and is often used by individuals preparing for their first GCP certification.
Provides a highly visual approach to understanding various GCP services and their use cases. It's particularly helpful for beginners and those who benefit from visual explanations of cloud concepts and how different services, accessed through the Cloud Console, fit together. It serves as a great supplementary resource for gaining a broad understanding.
This second edition provides updated and enhanced coverage of data engineering on GCP, including data governance services, which are managed through the Cloud Console. It's highly relevant for those looking to deepen their understanding of current data practices on GCP and valuable resource for data professionals.
A more introductory book specifically on BigQuery, this resource is good for those starting with data analytics on GCP and using the BigQuery interface within the Cloud Console. It provides practical examples and use cases for working with this powerful data service.
Targets a more advanced audience preparing for the Professional Cloud Architect certification. It covers designing and managing robust, scalable, and secure GCP solutions, requiring a deep understanding of various services accessible through the Cloud Console. It's highly relevant for professionals seeking to deepen their architectural knowledge.
Security is paramount in the cloud, and this book dives deep into GCP's security features and best practices, many of which are configured and monitored through the Cloud Console. It's crucial for anyone responsible for securing GCP environments and provides in-depth knowledge on topics like IAM, network security, and data security.
Published recently in late 2023, this book focuses on building cloud-native applications on Google Cloud, covering concepts like microservices, containerization, and leveraging various GCP services accessible through the Console. It addresses contemporary development practices and how to utilize GCP for scalable and secure applications, making it highly relevant for professionals.
Addresses the critical topic of migrating and modernizing legacy applications on Google Cloud, utilizing various services configured via the Cloud Console. It's highly relevant for professionals involved in cloud migration strategies and provides insights into transforming applications for a cloud-native environment.
For those interested in AI and Machine Learning on GCP, this book provides practical guidance on using GCP's AI services, accessible through the Cloud Console. It's relevant for understanding how to leverage GCP for AI workloads and can be valuable for students and professionals in data science and AI fields.
Authored by Google security experts, this book provides deep insights into the security practices and philosophies at Google that underpin GCP. While not solely focused on the Cloud Console, it offers essential context for understanding the security model of the platform you interact with through the console. It's valuable for anyone building critical systems on GCP.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/sd8h0r/build
Share
Similar courses
Similar courses are unavailable at this time. Please try again later.
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2025 OpenCourser