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Google Cloud Training
This is a self-paced lab that takes place in the Google Cloud console.
Read more
This is a self-paced lab that takes place in the Google Cloud console.
Read more
This is a self-paced lab that takes place in the Google Cloud console.
Vertex AI brings together the Google Cloud services for building ML under one, unified UI and API. In Vertex AI, you can now easily train and compare models using AutoML or custom code training and all your models are stored in one central model repository. These models can now be deployed to the same endpoints on Vertex AI.
AutoML Vision helps anyone with limited Machine Learning (ML) expertise train high quality image classification models. In this hands-on lab, you will learn how to produce a custom ML model that automatically recognizes damaged car parts.
Once you’ve produced your ML model, it’ll be immediately available for use. You can use the UI or the REST API to start generating predictions directly from the Google Cloud Console.
Register for this course and see more details by visiting:
OpenCourser.com/course/3anpyu/identify
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What's inside
Syllabus
Identify Damaged Car Parts with Vertex AutoML Vision
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops skills in using Google Cloud services for building Machine Learning (ML) models
Explores AutoML Vision, a tool that makes it easy for users with limited ML expertise to train image classification models
Provides hands-on labs for practical application of ML techniques
Introduces Vertex AI, a unified platform for ML development, training, and deployment
Taught by Google Cloud Training, an organization recognized for its expertise in cloud computing and ML
Save this course
Save Identify Damaged Car Parts with Vertex AutoML Vision to your list so you can find it easily later:
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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 Identify Damaged Car Parts with Vertex AutoML Vision with these
activities:
Review Image Processing Techniques
Show steps
Brush up on image processing techniques to enhance your understanding of the image classification process used in AutoML Vision.
Browse courses on
Image Processing
Show steps
-
Read articles or books on basic image processing concepts.
-
Complete online courses or tutorials on image processing.
-
Experiment with image processing libraries or tools.
Review Machine Learning Concepts
Show steps
Review the fundamentals of machine learning to ensure a strong foundation for the course.
Browse courses on
Machine Learning
Show steps
-
Read introductory articles or textbooks on machine learning concepts.
-
Complete online tutorials or courses on machine learning basics.
-
Practice implementing simple machine learning algorithms in a programming language.
Attend an AutoML Vision Workshop
Show steps
Accelerate your learning by attending a workshop or webinar focused on AutoML Vision.
Show steps
-
Research and identify upcoming AutoML Vision workshops or webinars.
-
Register for and attend the workshop or webinar.
-
Actively participate in the workshop or webinar, ask questions, and take notes.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore Vertex AI Documentation
Show steps
Familiarize yourself with the Vertex AI platform by exploring its documentation.
Show steps
-
Visit the Vertex AI documentation website.
-
Read through the tutorials and guides for AutoML Vision.
-
Experiment with the code samples provided in the documentation.
Participate in Discussion Forums
Show steps
Engage with other learners by participating in online discussion forums related to the course content.
Show steps
-
Join online discussion forums or communities related to machine learning or AutoML Vision.
-
Ask questions, share insights, and engage in discussions with other participants.
-
Seek support and guidance from experienced individuals in the community.
Create a Custom Image Classification Model
Show steps
Gain hands-on experience by creating a custom image classification model using AutoML Vision.
Show steps
-
Gather a dataset of images for the desired classification task.
-
Upload the dataset to the Vertex AI platform.
-
Create an AutoML Vision model and train it on the dataset.
-
Evaluate the performance of the model and make adjustments as needed.
Organize and Review Course Materials
Show steps
Stay organized and enhance your learning by compiling and reviewing course materials regularly.
Show steps
-
Create a system for organizing notes, assignments, and other course materials.
-
Review your notes and materials periodically to reinforce your understanding.
-
Summarize key concepts and ideas from the course materials.
Build a Web Application to Deploy the Model
Show steps
Apply your knowledge by building a web application that uses the trained model to classify images.
Show steps
-
Design the architecture of the web application.
-
Develop the front-end and back-end components.
-
Integrate the AutoML Vision model into the application.
-
Test and deploy the web application.
Review Image Processing Techniques
Show steps
Brush up on image processing techniques to enhance your understanding of the image classification process used in AutoML Vision.
Browse courses on
Image Processing
Show steps
Show steps
Show steps
- Read articles or books on basic image processing concepts.
- Complete online courses or tutorials on image processing.
- Experiment with image processing libraries or tools.
Review Machine Learning Concepts
Show steps
Review the fundamentals of machine learning to ensure a strong foundation for the course.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Read introductory articles or textbooks on machine learning concepts.
- Complete online tutorials or courses on machine learning basics.
- Practice implementing simple machine learning algorithms in a programming language.
Attend an AutoML Vision Workshop
Show steps
Accelerate your learning by attending a workshop or webinar focused on AutoML Vision.
Show steps
Show steps
Show steps
- Research and identify upcoming AutoML Vision workshops or webinars.
- Register for and attend the workshop or webinar.
- Actively participate in the workshop or webinar, ask questions, and take notes.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore Vertex AI Documentation
Show steps
Familiarize yourself with the Vertex AI platform by exploring its documentation.
Show steps
Show steps
Show steps
- Visit the Vertex AI documentation website.
- Read through the tutorials and guides for AutoML Vision.
- Experiment with the code samples provided in the documentation.
Participate in Discussion Forums
Show steps
Engage with other learners by participating in online discussion forums related to the course content.
Show steps
Show steps
Show steps
- Join online discussion forums or communities related to machine learning or AutoML Vision.
- Ask questions, share insights, and engage in discussions with other participants.
- Seek support and guidance from experienced individuals in the community.
Create a Custom Image Classification Model
Show steps
Gain hands-on experience by creating a custom image classification model using AutoML Vision.
Show steps
Show steps
Show steps
- Gather a dataset of images for the desired classification task.
- Upload the dataset to the Vertex AI platform.
- Create an AutoML Vision model and train it on the dataset.
- Evaluate the performance of the model and make adjustments as needed.
Organize and Review Course Materials
Show steps
Stay organized and enhance your learning by compiling and reviewing course materials regularly.
Show steps
Show steps
Show steps
- Create a system for organizing notes, assignments, and other course materials.
- Review your notes and materials periodically to reinforce your understanding.
- Summarize key concepts and ideas from the course materials.
Build a Web Application to Deploy the Model
Show steps
Apply your knowledge by building a web application that uses the trained model to classify images.
Show steps
Show steps
Show steps
- Design the architecture of the web application.
- Develop the front-end and back-end components.
- Integrate the AutoML Vision model into the application.
- Test and deploy the web application.
Career center
Learners who complete Identify Damaged Car Parts with Vertex AutoML Vision will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, implement, and maintain machine learning systems. This course may be useful to Machine Learning Engineers as it provides them with the skills to train and deploy a custom ML model that can be used to identify damaged car parts. This can help Machine Learning Engineers to develop new products and services that can improve the safety and efficiency of the automotive industry.
Automotive Engineer
Automotive Engineer
Automotive Engineers design, develop, and test vehicles and their components. This course may be useful to Automotive Engineers as it provides them with the skills to use machine learning to improve the safety and efficiency of vehicles. This can help Automotive Engineers to develop new technologies that can benefit the automotive industry and the public at large.
Research Scientist
Research Scientist
Research Scientists conduct research and development in various scientific and technological fields. This course may be useful to Research Scientists as it provides them with the skills to develop new machine learning algorithms and models. This can help Research Scientists to advance the field of machine learning and develop new technologies that can benefit society.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course may be useful to Software Engineers as it provides them with the skills to develop and deploy a custom ML model. This can help Software Engineers to create new software systems that can improve the safety and efficiency of the automotive industry.
Data Scientist
Data Scientist
Data Scientists use scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured. This course may be useful to Data Scientists as it provides them with the skills to train and deploy a custom ML model that can be used to identify damaged car parts. This can help Data Scientists to develop new products and services that can improve the safety and efficiency of the automotive industry.
Software Developer
Software Developer
Software Developers design, develop, and maintain software applications. This course may be useful to Software Developers as it provides them with the skills to develop and deploy a custom ML model. This can help Software Developers to create new software applications that can improve the safety and efficiency of the automotive industry.
Quality Assurance Analyst
Quality Assurance Analyst
Quality Assurance Analysts test and evaluate software products to ensure that they meet the required standards. This course may be useful to Quality Assurance Analysts as it provides them with the skills to assess the accuracy and reliability of a custom ML model. This can help Quality Assurance Analysts to ensure that the software products they are testing are of the highest quality possible.
Manufacturing Engineer
Manufacturing Engineer
Manufacturing Engineers design and manage the production of goods and services. This course may be useful to Manufacturing Engineers as it provides them with the skills to use machine learning to improve the efficiency of their manufacturing processes. This can help Manufacturing Engineers to reduce costs and improve the quality of their products.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products and services. This course may be useful to Product Managers as it provides them with the skills to identify and assess the needs of their customers. This can help Product Managers to develop products and services that are in high demand and that meet the needs of the market.
Project Manager
Project Manager
Project Managers plan, organize, and manage projects. This course may be useful to Project Managers as it provides them with the skills to manage the development and deployment of a custom ML model. This can help Project Managers to ensure that their projects are completed on time, within budget, and to the required specifications.
Solutions Architect
Solutions Architect
Solutions Architects design and implement technical solutions to meet the needs of their customers. This course may be useful to Solutions Architects as it provides them with the skills to assess the needs of their customers and develop solutions that meet those needs. This can help Solutions Architects to develop successful solutions that meet the business objectives of their customers.
Technical Writer
Technical Writer
Technical Writers create and maintain technical documentation. This course may be useful to Technical Writers as it provides them with the skills to communicate complex technical information in a clear and concise manner. This can help Technical Writers to create documentation that is easy to understand and use.
User Experience Designer
User Experience Designer
User Experience Designers design and evaluate user interfaces for software products. This course may be useful to User Experience Designers as it provides them with the skills to assess the needs of their users and design interfaces that are easy to use and understand. This can help User Experience Designers to create software products that are enjoyable and efficient to use.
Data Engineer
Data Engineer
Data Engineers design, construct, and maintain data management systems. This course may be useful to Data Engineers as it provides them with the skills to build a custom ML model that can be used to automate the process of identifying damaged car parts. This can help Data Engineers to improve the efficiency of their data management systems and free up their time to focus on other tasks.
Data Analyst
Data Analyst
Data Analysts translate raw data into usable information using statistical analysis and software. This course may be useful to Data Analysts as it provides them with the skills to assess the accuracy of their data through the training of an ML model. This is useful for making sure that the data used by Data Analysts is of the highest quality possible, leading to more accurate and actionable insights.
For more career information including salaries, visit:
OpenCourser.com/course/3anpyu/identify
Reading list
We've selected ten 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
Identify Damaged Car Parts with Vertex AutoML Vision.
Provides a comprehensive overview of machine learning concepts and techniques, using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It covers various aspects of machine learning, including data preprocessing, feature engineering, model selection, and evaluation.
Focuses on deep learning, a subfield of machine learning that uses artificial neural networks. It provides a practical guide to building and training deep learning models using Python libraries such as Keras and TensorFlow.
Save
Provides a comprehensive overview of computer vision algorithms and applications. It covers various aspects of computer vision, including image processing, object detection, and image recognition.
Provides a comprehensive overview of statistical learning methods. It covers various topics such as linear regression, logistic regression, decision trees, and support vector machines.
Provides a comprehensive overview of machine learning in Python. It covers various aspects of machine learning, including data preprocessing, feature engineering, model selection, and evaluation.
Covers machine learning concepts and techniques using the R programming language. It provides a practical guide to building and training machine learning models using R libraries such as caret and mlr.
Provides a comprehensive overview of natural language processing (NLP) in Python. It covers various aspects of NLP, including text preprocessing, text classification, and sentiment analysis.
Provides a practical introduction to data science using Python. It covers various aspects of data science, including data cleaning, data exploration, and machine learning.
Provides a gentle introduction to machine learning concepts and algorithms. It covers the basics of machine learning, including supervised and unsupervised learning, regression, and classification.
Provides a practical introduction to machine learning for non-technical readers. It covers the basics of machine learning, including supervised and unsupervised learning, regression, and classification.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/3anpyu/identify
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Effort
1 hour 30 minutes
Level
Intermediate
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Good to know
Develops skills in using Google Cloud services for building Machine Learning (ML) models
Explores AutoML Vision, a tool that makes it easy for users with limited ML expertise to train image classification models
Provides hands-on labs for practical application of ML techniques
Introduces Vertex AI, a unified platform for ML development, training, and deployment
Taught by Google Cloud Training, an organization recognized for its expertise in cloud computing and ML
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