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Google Cloud Training
This is a self-paced lab that takes place in the Google Cloud console.
In this lab, you explore and evaluate AI models using Model Garden.
Register for this course and see more details by visiting:
OpenCourser.com/course/9qjh3v/explore
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What's inside
Syllabus
Explore and Evaluate Models using Model Garden
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides learners with the skills to evaluate AI models efficiently utilizing Model Garden
Suitable for learners interested in Machine Learning and model evaluation techniques
Taught by Google Cloud Training, recognized experts in the field
Can be completed at the learner's desired pace, allowing for flexibility in learning
May require prior knowledge in Machine Learning or AI, learners new to the field may need additional resources
Involvement of Google Cloud Training ensures the course materials are up-to-date with industry best practices
Save this course
Save Explore and Evaluate Models using Model Garden 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 Explore and Evaluate Models using Model Garden with these
activities:
Review Machine Learning Concepts
Show steps
Refreshing your understanding of machine learning concepts will provide a stronger foundation for exploring Model Garden's capabilities.
Browse courses on
Machine Learning
Show steps
-
Revisit key concepts such as supervised and unsupervised learning
-
Review common machine learning algorithms
Create a Model Garden Resource Compilation
Show steps
Organizing and compiling resources on Model Garden will enhance your ability to find and use relevant information.
Browse courses on
Knowledge Management
Show steps
-
Gather documentation, tutorials, and code samples related to Model Garden
-
Create a structured resource repository
-
Share the compilation with other learners
Analyze Model Garden Use Cases
Show steps
Analyzing real-world use cases of Model Garden will reinforce your understanding of its capabilities and limitations.
Show steps
-
Explore Model Garden documentation and tutorials
-
Identify industries and applications where Model Garden is used
-
Analyze how Model Garden integrates with other Google Cloud services
Four other activities
Expand to see all activities and additional details
Show all seven activities
Develop a Model Garden Deployment Plan
Show steps
Creating a deployment plan will solidify your understanding of the process and best practices involved in deploying models with Model Garden.
Browse courses on
Cloud Deployment
Show steps
-
Research best practices for deploying AI models
-
Identify the target environment for deployment
-
Create a step-by-step deployment plan
-
Test and evaluate the deployment plan
Build a Custom Model Garden Web Application
Show steps
Building a web application that utilizes Model Garden will provide hands-on experience in integrating models into a functional product.
Browse courses on
Web Development
Show steps
-
Design the web application architecture
-
Develop the front-end interface
-
Integrate Model Garden into the application
-
Deploy and test the web application
Volunteer as a Model Garden Mentor
Show steps
Mentoring others on Model Garden will deepen your own understanding and contribute to the AI community.
Show steps
-
Join online forums or discussion groups on Model Garden
-
Offer guidance and support to learners
-
Share your own experiences and insights
Contribute to Model Garden Open Source Projects
Show steps
Contributing to open source projects related to Model Garden will provide real-world experience and enhance your understanding of the platform.
Browse courses on
GitHub
Show steps
-
Identify open source projects related to Model Garden
-
Review the project documentation and codebase
-
Submit bug reports or feature requests
-
Contribute code or documentation improvements
Review Machine Learning Concepts
Show steps
Refreshing your understanding of machine learning concepts will provide a stronger foundation for exploring Model Garden's capabilities.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Revisit key concepts such as supervised and unsupervised learning
- Review common machine learning algorithms
Create a Model Garden Resource Compilation
Show steps
Organizing and compiling resources on Model Garden will enhance your ability to find and use relevant information.
Browse courses on
Knowledge Management
Show steps
Show steps
Show steps
- Gather documentation, tutorials, and code samples related to Model Garden
- Create a structured resource repository
- Share the compilation with other learners
Analyze Model Garden Use Cases
Show steps
Analyzing real-world use cases of Model Garden will reinforce your understanding of its capabilities and limitations.
Show steps
Show steps
Show steps
- Explore Model Garden documentation and tutorials
- Identify industries and applications where Model Garden is used
- Analyze how Model Garden integrates with other Google Cloud services
Four other activities
Expand to see all activities and additional details
Show all seven activities
Develop a Model Garden Deployment Plan
Show steps
Creating a deployment plan will solidify your understanding of the process and best practices involved in deploying models with Model Garden.
Browse courses on
Cloud Deployment
Show steps
Show steps
Show steps
- Research best practices for deploying AI models
- Identify the target environment for deployment
- Create a step-by-step deployment plan
- Test and evaluate the deployment plan
Build a Custom Model Garden Web Application
Show steps
Building a web application that utilizes Model Garden will provide hands-on experience in integrating models into a functional product.
Browse courses on
Web Development
Show steps
Show steps
Show steps
- Design the web application architecture
- Develop the front-end interface
- Integrate Model Garden into the application
- Deploy and test the web application
Volunteer as a Model Garden Mentor
Show steps
Mentoring others on Model Garden will deepen your own understanding and contribute to the AI community.
Show steps
Show steps
Show steps
- Join online forums or discussion groups on Model Garden
- Offer guidance and support to learners
- Share your own experiences and insights
Contribute to Model Garden Open Source Projects
Show steps
Contributing to open source projects related to Model Garden will provide real-world experience and enhance your understanding of the platform.
Browse courses on
GitHub
Show steps
Show steps
Show steps
- Identify open source projects related to Model Garden
- Review the project documentation and codebase
- Submit bug reports or feature requests
- Contribute code or documentation improvements
Career center
Learners who complete Explore and Evaluate Models using Model Garden will develop knowledge and skills
that may be useful to these careers:
AI Engineer
AI Engineer
AI Engineers apply machine learning and artificial intelligence techniques to solve business problems. **Explore and Evaluate Models using Model Garden** provides an introduction to model evaluation, a key aspect of AI development, helping aspiring AI Engineers build a foundation in this field.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to provide insights for decision-making. **Explore and Evaluate Models using Model Garden** offers an opportunity to gain practical experience with model evaluation, a valuable skill for Statisticians.
Data Science Consultant
Data Science Consultant
Data Science Consultants provide expertise in data science and analytics to help businesses solve problems and make informed decisions. **Explore and Evaluate Models using Model Garden** can provide valuable hands-on experience with model evaluation, a key skill for Data Science Consultants.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models to solve real-world problems. The **Explore and Evaluate Models using Model Garden** course offers an opportunity to gain hands-on experience with model evaluation and selection, a critical skill for Machine Learning Engineers.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data to make investment recommendations and provide financial advice. **Explore and Evaluate Models using Model Garden** may provide helpful insights into model evaluation and selection, which are relevant skills for Financial Analysts.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to improve business operations. **Explore and Evaluate Models using Model Garden** may provide helpful insights into model evaluation and selection, which are relevant skills for Operations Research Analysts.
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, analyzing, and interpreting data to help businesses make informed decisions. **Explore and Evaluate Models using Model Garden** can provide a valuable introduction to the techniques and tools used in data science, helping aspiring Data Scientists build a foundation in this field.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze financial data and make investment decisions. **Explore and Evaluate Models using Model Garden** may provide helpful insights into model evaluation and selection, which are relevant skills for Quantitative Analysts.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. While **Explore and Evaluate Models using Model Garden** primarily focuses on model evaluation, the data analysis and statistical skills gained in this course can be helpful for aspiring Actuaries.
Data Engineer
Data Engineer
Data Engineers design and maintain data systems and infrastructure. While **Explore and Evaluate Models using Model Garden** primarily focuses on model evaluation, the data management skills gained in this course can be helpful for aspiring Data Engineers.
Data Analyst
Data Analyst
Data Analysts clean, analyze, and visualize data to extract insights and trends. While **Explore and Evaluate Models using Model Garden** primarily focuses on model evaluation, the skills gained in this course, such as data exploration and analysis, can be applied to the broader field of data analysis.
Market Researcher
Market Researcher
Market Researchers collect and analyze data to understand market trends and consumer behavior. While **Explore and Evaluate Models using Model Garden** focuses on model evaluation, the data analysis and interpretation skills gained in this course can be applied to market research roles.
Business Analyst
Business Analyst
Business Analysts identify and solve business problems by analyzing data and providing recommendations. While **Explore and Evaluate Models using Model Garden** focuses on model evaluation, the data analysis and problem-solving skills gained in this course can be applied to business analysis roles.
Product Manager
Product Manager
Product Managers lead the development and launch of new products and services. While **Explore and Evaluate Models using Model Garden** primarily focuses on model evaluation, the understanding of data and analytics gained in this course can be valuable for Product Managers.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. While **Explore and Evaluate Models using Model Garden** focuses on model evaluation, the programming and problem-solving skills gained in this course can be applied to software engineering roles.
For more career information including salaries, visit:
OpenCourser.com/course/9qjh3v/explore
Reading list
We've selected eight 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
Explore and Evaluate Models using Model Garden.
Provides a comprehensive introduction to machine learning with TensorFlow, covering topics such as data preprocessing, model training, and evaluation. It great resource for learners who want to gain a solid foundation in machine learning and TensorFlow.
Covers a wide range of machine learning topics, including supervised and unsupervised learning, model selection, and feature engineering. It valuable resource for learners who want to gain a practical understanding of machine learning algorithms and their implementation in Python.
Provides a comprehensive introduction to deep learning with Python, covering topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for learners who want to gain a deeper understanding of deep learning and its applications.
Provides a comprehensive introduction to natural language processing with Python, covering topics such as text preprocessing, part-of-speech tagging, and named entity recognition. It valuable resource for learners who want to gain a practical understanding of how to use NLP techniques for various tasks.
Save
Provides a comprehensive overview of computer vision algorithms and applications, covering topics such as image processing, object detection, and image recognition. It valuable resource for learners who want to gain a deeper understanding of the theoretical foundations of computer vision.
Save
Provides a comprehensive overview of generative adversarial networks (GANs), covering topics such as GAN architectures, training GANs, and applications of GANs. It valuable resource for learners who want to gain a deeper understanding of GANs and their applications.
Provides a gentle introduction to machine learning for absolute beginners, covering topics such as the basics of machine learning, different types of machine learning algorithms, and how to use machine learning for various tasks. It valuable resource for learners who are new to machine learning and want to gain a basic understanding of the field.
Provides a practical introduction to deep learning with R, covering topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for learners who want to gain a practical understanding of how to use deep learning for various tasks in R.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/9qjh3v/explore
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Effort
60 minutes
Level
Intermediate
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Good to know
Provides learners with the skills to evaluate AI models efficiently utilizing Model Garden
Suitable for learners interested in Machine Learning and model evaluation techniques
Taught by Google Cloud Training, recognized experts in the field
Can be completed at the learner's desired pace, allowing for flexibility in learning
May require prior knowledge in Machine Learning or AI, learners new to the field may need additional resources
Involvement of Google Cloud Training ensures the course materials are up-to-date with industry best practices
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