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Google Cloud Training
Learn how to take advantage of Google Cloud and Vertex AI's Generative AI Studio to prototype and customize generative AI models for your applications with Udacity.
Register for this course and see more details by visiting:
OpenCourser.com/course/2i4020/introduction
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What's inside
Syllabus
This course introduces Generative AI Studio, a product on Vertex AI, that helps you prototype and customize generative AI models so you can use their capabilities in your applications.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Exposes practitioners to generative AI models and applications, making them more effective in their work
Highly relevant to those working in data science, machine learning, and related fields
Taught by Google Cloud Training, who are recognized for their work in generative AI
Provides hands-on labs and interactive materials for practical experience
Part of a larger series of courses, indicating comprehensiveness
May require learners to come in with some background knowledge or experience in AI
Save this course
Save Introduction to Gen AI Studio with Google Cloud 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 Introduction to Gen AI Studio with Google Cloud with these
activities:
Review Fundamentals of Machine Learning
Show steps
Refresh your understanding of machine learning concepts to better grasp the principles behind Generative AI.
Browse courses on
Machine Learning
Show steps
-
Revisit textbooks or online materials on supervised and unsupervised learning algorithms.
-
Explore code examples and walk through the steps of implementing basic machine learning models.
-
Solve practice problems or exercises to reinforce your knowledge.
-
Discuss key concepts with other students or a mentor to clarify any doubts.
-
Take an online course or workshop to get a structured refresher on machine learning.
Code Along with Generative AI Studio Demos
Show steps
Deepen your understanding of Generative AI Studio features by practicing with hands-on code examples.
Show steps
-
Navigate to the Generative AI Studio documentation.
-
Select a demo or tutorial that interests you.
-
Follow the step-by-step instructions to build and run code examples.
-
Troubleshoot any errors you encounter.
-
Experiment with different code parameters to observe their effects.
Develop a Generative AI Model Prototype
Show steps
Solidify your understanding of the fundamentals of Generative AI Studio by creating a prototype model.
Show steps
-
Define a problem statement that your model will address.
-
Select an appropriate dataset and explore its characteristics.
-
Choose a Generative AI model type and configure its parameters.
-
Train and evaluate the model using the Generative AI Studio interface.
-
Refine the model's performance by optimizing hyperparameters and experimenting with different architectures.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Explore Advanced Techniques in Generative AI Studio
Show steps
Enhance your knowledge of advanced features in Generative AI Studio through guided tutorials.
Show steps
-
Access the Generative AI Studio documentation and tutorials section.
-
Identify tutorials that cover advanced topics such as model fine-tuning, ensemble learning, and interpretability.
-
Follow the tutorials step-by-step, implementing the techniques and concepts.
-
Experiment with different parameters and settings to observe their impact on model performance.
-
Share your learnings and insights with other students or the online community.
Design a Generative AI System for a Real-World Problem
Show steps
Apply your knowledge of Generative AI Studio to a practical problem and develop a comprehensive solution.
Show steps
-
Identify a real-world challenge that can be addressed with Generative AI.
-
Research and gather data relevant to the problem.
-
Design a Generative AI system architecture, outlining its components and their interrelationships.
-
Develop and train a generative AI model for the specific problem.
-
Create a user interface or integration for interacting with the Generative AI system.
Contribute to Generative AI Open-Source Projects
Show steps
Deepen your understanding of Generative AI and contribute to the open-source community.
Show steps
-
Familiarize yourself with open-source projects related to Generative AI, such as Hugging Face or OpenAI.
-
Identify areas where you can contribute to existing projects, such as code improvements, documentation updates, or new features.
-
Fork the project repository and make your changes in a dedicated branch.
-
Submit a pull request with your contributions, following the project's guidelines.
-
Collaborate with other contributors to refine your contributions and ensure they are accepted.
Build a Portfolio of Generative AI Projects
Show steps
Showcase your skills and creativity by creating a portfolio of diverse generative AI projects.
Show steps
-
Brainstorm ideas for generative AI projects that demonstrate different capabilities and applications.
-
Develop and refine each project using Generative AI Studio.
-
Create a portfolio website or platform to showcase your projects.
-
Write detailed project descriptions highlighting the problem addressed, technical details, and outcomes.
-
Present your portfolio to potential employers or clients.
Review Fundamentals of Machine Learning
Show steps
Refresh your understanding of machine learning concepts to better grasp the principles behind Generative AI.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Revisit textbooks or online materials on supervised and unsupervised learning algorithms.
- Explore code examples and walk through the steps of implementing basic machine learning models.
- Solve practice problems or exercises to reinforce your knowledge.
- Discuss key concepts with other students or a mentor to clarify any doubts.
- Take an online course or workshop to get a structured refresher on machine learning.
Code Along with Generative AI Studio Demos
Show steps
Deepen your understanding of Generative AI Studio features by practicing with hands-on code examples.
Show steps
Show steps
Show steps
- Navigate to the Generative AI Studio documentation.
- Select a demo or tutorial that interests you.
- Follow the step-by-step instructions to build and run code examples.
- Troubleshoot any errors you encounter.
- Experiment with different code parameters to observe their effects.
Develop a Generative AI Model Prototype
Show steps
Solidify your understanding of the fundamentals of Generative AI Studio by creating a prototype model.
Show steps
Show steps
Show steps
- Define a problem statement that your model will address.
- Select an appropriate dataset and explore its characteristics.
- Choose a Generative AI model type and configure its parameters.
- Train and evaluate the model using the Generative AI Studio interface.
- Refine the model's performance by optimizing hyperparameters and experimenting with different architectures.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Explore Advanced Techniques in Generative AI Studio
Show steps
Enhance your knowledge of advanced features in Generative AI Studio through guided tutorials.
Show steps
Show steps
Show steps
- Access the Generative AI Studio documentation and tutorials section.
- Identify tutorials that cover advanced topics such as model fine-tuning, ensemble learning, and interpretability.
- Follow the tutorials step-by-step, implementing the techniques and concepts.
- Experiment with different parameters and settings to observe their impact on model performance.
- Share your learnings and insights with other students or the online community.
Design a Generative AI System for a Real-World Problem
Show steps
Apply your knowledge of Generative AI Studio to a practical problem and develop a comprehensive solution.
Show steps
Show steps
Show steps
- Identify a real-world challenge that can be addressed with Generative AI.
- Research and gather data relevant to the problem.
- Design a Generative AI system architecture, outlining its components and their interrelationships.
- Develop and train a generative AI model for the specific problem.
- Create a user interface or integration for interacting with the Generative AI system.
Contribute to Generative AI Open-Source Projects
Show steps
Deepen your understanding of Generative AI and contribute to the open-source community.
Show steps
Show steps
Show steps
- Familiarize yourself with open-source projects related to Generative AI, such as Hugging Face or OpenAI.
- Identify areas where you can contribute to existing projects, such as code improvements, documentation updates, or new features.
- Fork the project repository and make your changes in a dedicated branch.
- Submit a pull request with your contributions, following the project's guidelines.
- Collaborate with other contributors to refine your contributions and ensure they are accepted.
Build a Portfolio of Generative AI Projects
Show steps
Showcase your skills and creativity by creating a portfolio of diverse generative AI projects.
Show steps
Show steps
Show steps
- Brainstorm ideas for generative AI projects that demonstrate different capabilities and applications.
- Develop and refine each project using Generative AI Studio.
- Create a portfolio website or platform to showcase your projects.
- Write detailed project descriptions highlighting the problem addressed, technical details, and outcomes.
- Present your portfolio to potential employers or clients.
Career center
Learners who complete Introduction to Gen AI Studio with Google Cloud will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist is responsible for collecting, organizing, and analyzing complex data to find meaningful patterns and trends. They build AI models to solve important business problems and stay up-to-date on the latest developments in AI.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer is responsible for building and deploying machine learning models that are customized to specific business needs. The course will introduce you to Generative AI Studio, a product on Vertex AI, that will help you prototype and customize generative AI models that can be used to enhance your machine learning capabilities.
AI Engineer
AI Engineer
An AI Engineer is responsible for designing, developing, and implementing AI systems. The course introduces Generative AI Studio, a product on Vertex AI, that helps you prototype and customize generative AI models so you can use their capabilities in your AI systems.
Data Analyst
Data Analyst
A Data Analyst is responsible for collecting, cleaning, and analyzing data to derive meaningful insights. The course will introduce you to Generative AI Studio, a product on Vertex AI, that will help you create generative AI models that will help you analyze data.
Product Manager
Product Manager
A Product Manager is responsible for overseeing the development of a product from conception to launch. The course introduces Generative AI Studio, a product on Vertex AI, that helps you prototype and customize generative AI models that can be used to enhance your product.
Software Engineer
Software Engineer
A Software Engineer is responsible for designing, developing, and testing computer programs. The course introduces Generative AI Studio, a product on Vertex AI, that helps you prototype and customize generative AI models that can be used to increase your productivity as a software engineer.
Business Analyst
Business Analyst
A Business Analyst is responsible for analyzing business processes and identifying opportunities for improvement. The course introduces Generative AI Studio, a product on Vertex AI, that helps you prototype and customize generative AI models that can be used to analyze business data and make better decisions.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst is responsible for using mathematical and statistical models to analyze financial data. The course introduces Generative AI Studio, a product on Vertex AI, that helps you prototype and customize generative AI models that can be used to enhance your financial analysis.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst is responsible for using mathematical and analytical techniques to solve complex business problems. The course introduces Generative AI Studio, a product on Vertex AI, that helps you prototype and customize generative AI models that can be used to solve complex business problems.
Market Researcher
Market Researcher
A Market Researcher is responsible for collecting and analyzing data about consumer behavior. The course introduces Generative AI Studio, a product on Vertex AI, that helps you prototype and customize generative AI models that can be used to collect and analyze consumer data.
User Experience Researcher
User Experience Researcher
A User Experience Researcher is responsible for studying user behavior and providing insights on how to improve the user experience. The course introduces Generative AI Studio, a product on Vertex AI, that helps you prototype and customize generative AI models that can be used to analyze user behavior and provide insights on how to improve the user experience.
Data Engineer
Data Engineer
A Data Engineer is responsible for designing and building data pipelines and infrastructure. The course introduces Generative AI Studio, a product on Vertex AI, that helps you prototype and customize generative AI models that can be used to create and manage data pipelines and infrastructure.
Web Developer
Web Developer
A Web Developer is responsible for designing and developing websites. The course introduces Generative AI Studio, a product on Vertex AI, that helps you prototype and customize generative AI models that can be used to create and enhance websites.
Mobile Developer
Mobile Developer
A Mobile Developer is responsible for designing and developing mobile applications. The course introduces Generative AI Studio, a product on Vertex AI, that helps you prototype and customize generative AI models that can be used to create and enhance mobile applications.
Game Developer
Game Developer
A Game Developer is responsible for designing and developing video games. The course introduces Generative AI Studio, a product on Vertex AI, that helps you prototype and customize generative AI models that can be used to create and enhance video games.
For more career information including salaries, visit:
OpenCourser.com/course/2i4020/introduction
Reading list
We've selected seven 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
Introduction to Gen AI Studio with Google Cloud.
Provides a hands-on guide to deep learning, covering the fundamentals of neural networks, convolutional neural networks, recurrent neural networks, and more. It valuable resource for anyone who wants to build and train their own generative AI models.
Provides a comprehensive overview of natural language processing, covering topics such as tokenization, stemming, lemmatization, parsing, and machine translation. It valuable resource for anyone who wants to develop generative AI models for natural language applications.
Save
Introduces generative AI models for healthcare, including models for drug discovery, disease diagnosis, and treatment planning.
Provides a hands-on introduction to deep learning using the Fastai and PyTorch libraries. It valuable resource for anyone who wants to learn how to build and train their own deep learning models.
Provides an overview of artificial intelligence techniques for generative AI.
Provides a hands-on introduction to machine learning using the Scikit-Learn, Keras, and TensorFlow libraries. It valuable resource for anyone who wants to learn how to build and train their own machine learning models.
Provides a gentle introduction to machine learning for beginners. It covers the basics of machine learning, including supervised learning, unsupervised learning, and reinforcement learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/2i4020/introduction
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Effort
35 minutes
Via
Udacity
Instructor
Google Cloud Training
Language
English
Good to know
Exposes practitioners to generative AI models and applications, making them more effective in their work
Highly relevant to those working in data science, machine learning, and related fields
Taught by Google Cloud Training, who are recognized for their work in generative AI
Provides hands-on labs and interactive materials for practical experience
Part of a larger series of courses, indicating comprehensiveness
May require learners to come in with some background knowledge or experience in AI
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