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Google Cloud Training

This is a self-paced lab that takes place in the Google Cloud console. This lab will provide an introductory, hands-on experience with Generative AI on Google Cloud.

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What's inside

Syllabus

Generative AI with Vertex AI: Getting Started

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
This course is great for those who want to learn about generative AI, as it uses Google Cloud console for hands-on application

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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 Generative AI with Vertex AI: Getting Started with these activities:
Review of probability and statistics
Refresh your knowledge of probability and statistics to reinforce concepts essential for generative AI.
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  • Review key concepts in probability and statistics.
  • Complete practice problems and exercises.
  • Relate probability and statistics to generative AI concepts.
Refresher on Python coding
Refresh your foundational understanding of Python coding to prepare for the course.
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  • Review the basics of Python syntax and data structures.
  • Complete a few coding exercises to practice writing Python code.
Organize course materials
Organize your notes, assignments, and other course materials to enhance your learning and retention.
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  • Review your notes and materials from each class.
  • Create a system for organizing your materials, such as folders or a digital notebook.
  • Keep your materials up-to-date and accessible.
  • Use your organized materials to study and prepare for assessments.
Five other activities
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Resource repository for Generative AI
Gather useful resources and tools related to Generative AI to enhance your learning experience.
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  • Search for and collect relevant articles, tutorials, and code samples.
  • Organize your resources into a structured format.
  • Share your compilation with other learners.
Introduction to TensorFlow tutorial
Use this tutorial to build a foundation in TensorFlow and gain a better understanding of machine learning and generative AI.
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  • Follow the step-by-step instructions in the TensorFlow tutorial.
  • Experiment with different parameters and settings.
  • Apply what you've learned to solve a real-world problem.
Participate in online forums
Engage in discussions to reinforce your understanding of generative AI concepts and assist other learners.
Show steps
  • Join online forums or discussion groups related to generative AI.
  • Actively participate in discussions and answer questions.
  • Share your knowledge and insights with other learners.
Coding exercises on Generative AI
Complete these coding exercises to enhance your understanding and practical skills in Generative AI.
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  • Work through a set of coding exercises on generative AI.
  • Implement generative AI algorithms in Python.
  • Experiment with different generative AI techniques.
Generative AI project
Put your skills to the test by building a generative AI project that showcases your learning.
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  • Define the scope and goals of your project.
  • Gather and prepare the necessary data.
  • Choose and implement generative AI models.
  • Evaluate and iterate on your results.
  • Present your project findings.

Career center

Learners who complete Generative AI with Vertex AI: Getting Started will develop knowledge and skills that may be useful to these careers:
Data Scientist
Professionals in this role collect, analyze, and interpret large sets of data to uncover patterns and trends. They use statistical modeling and machine learning techniques to build predictive models that can be used to make informed decisions. The Generative AI course offered by Google Cloud provides a strong foundation in generative AI and machine learning, which are essential skills for Data Scientists. The course can help Data Scientists develop expertise in using Vertex AI, Google Cloud's platform for building and deploying ML models. This expertise can give Data Scientists a significant advantage in the job market and help them succeed in this high-demand field.
Machine Learning Engineer
Machine Learning Engineers work on the design, development, and deployment of machine learning models. They use their knowledge of machine learning algorithms and cloud computing platforms to build models that can solve complex business problems. This course provides a hands-on introduction to Generative AI on Google Cloud, covering topics like text generation, image generation, and translation. This knowledge can be directly applied to building ML models that can generate new data, enhance existing data, and improve decision-making. The course can help Machine Learning Engineers gain the skills necessary to succeed in this rapidly growing field.
AI Engineer
AI Engineers design, develop, and maintain AI systems. They work on a variety of projects, from self-driving cars to speech recognition systems. This course provides a strong foundation in the fundamentals of Generative AI, including topics such as generative adversarial networks (GANs) and variational autoencoders (VAEs). This knowledge is essential for AI Engineers who want to build and deploy AI systems that can generate new data, enhance existing data, and improve decision-making. The course can help AI Engineers gain the skills necessary to succeed in this cutting-edge field.
Software Engineer
Software Engineers design, develop, and maintain software applications. They work on a variety of projects, from mobile apps to enterprise software. This course provides a hands-on introduction to Generative AI on Google Cloud, covering topics like text generation, image generation, and translation. This knowledge can be directly applied to developing software applications that can generate new content, enhance user experiences, and improve decision-making. The course can help Software Engineers gain the skills necessary to succeed in this rapidly growing field.
Data Analyst
Data Analysts collect, analyze, and interpret data to identify trends and patterns. They use their findings to make recommendations to businesses on how to improve their operations. This course provides a strong foundation in the fundamentals of Generative AI, including topics such as generative adversarial networks (GANs) and variational autoencoders (VAEs). This knowledge can be used to develop new methods for data analysis and to improve the accuracy and efficiency of existing methods. The course can help Data Analysts gain the skills necessary to succeed in this important field.
Product Manager
Product Managers are responsible for the development and launch of new products and services. They work with engineers, designers, and marketers to bring products to market that meet the needs of customers. This course provides a strong foundation in the fundamentals of Generative AI, including topics such as generative adversarial networks (GANs) and variational autoencoders (VAEs). This knowledge can be used to develop new products and services that leverage the power of Generative AI to generate new content, enhance user experiences, and improve decision-making. The course can help Product Managers gain the skills necessary to succeed in this exciting field.
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. They use their findings to make recommendations to investors on how to allocate their money. This course provides a strong foundation in the fundamentals of Generative AI, including topics such as generative adversarial networks (GANs) and variational autoencoders (VAEs). This knowledge can be used to develop new methods for financial analysis and to improve the accuracy and efficiency of existing methods. The course can help Quantitative Analysts gain the skills necessary to succeed in this challenging field.
Business Analyst
Business Analysts help businesses understand their needs and develop solutions to meet those needs. They use a variety of techniques, including data analysis, process mapping, and stakeholder interviews. This course provides a strong foundation in the fundamentals of Generative AI, including topics such as generative adversarial networks (GANs) and variational autoencoders (VAEs). This knowledge can be used to develop new methods for business analysis and to improve the accuracy and efficiency of existing methods. The course can help Business Analysts gain the skills necessary to succeed in this important field.
Consultant
Consultants help businesses solve problems and improve their performance. They work with clients to identify opportunities and develop solutions. This course provides a strong foundation in the fundamentals of Generative AI, including topics such as generative adversarial networks (GANs) and variational autoencoders (VAEs). This knowledge can be used to develop new methods for consulting and to improve the accuracy and efficiency of existing methods. The course can help Consultants gain the skills necessary to succeed in this challenging field.
Market Researcher
Market Researchers gather and analyze data about consumers and markets. They use their findings to help businesses make informed decisions about product development, marketing, and pricing. This course provides a strong foundation in the fundamentals of Generative AI, including topics such as generative adversarial networks (GANs) and variational autoencoders (VAEs). This knowledge can be used to develop new methods for market research and to improve the accuracy and efficiency of existing methods. The course can help Market Researchers gain the skills necessary to succeed in this important field.
Financial Analyst
Financial Analysts use financial data to make recommendations to investors and businesses. They analyze financial statements, economic data, and market trends to identify opportunities and risks. This course provides a strong foundation in the fundamentals of Generative AI, including topics such as generative adversarial networks (GANs) and variational autoencoders (VAEs). This knowledge can be used to develop new methods for financial analysis and to improve the accuracy and efficiency of existing methods. The course can help Financial Analysts gain the skills necessary to succeed in this challenging field.
Actuary
Actuaries use mathematics and statistics to assess risk and uncertainty. They work with insurance companies, pension funds, and other financial institutions to develop products and services that protect against financial loss. This course provides a strong foundation in the fundamentals of Generative AI, including topics such as generative adversarial networks (GANs) and variational autoencoders (VAEs). This knowledge can be used to develop new methods for actuarial science and to improve the accuracy and efficiency of existing methods. The course can help Actuaries gain the skills necessary to succeed in this important field.

Reading list

We've selected 15 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 Generative AI with Vertex AI: Getting Started.
Provides a comprehensive overview of speech and language processing, which type of generative AI. It would be a valuable reference for this course, providing additional depth and breadth.
Provides a comprehensive overview of GANs, which are a type of generative AI. It would be a valuable reference for this course, providing additional depth and breadth.
Provides a comprehensive overview of autonomous mobile robots, covering their history, key concepts, and techniques. It good reference for learners who want to gain a deeper understanding of the field.
Provides a comprehensive overview of deep learning, covering its history, key concepts, and techniques. It good reference for learners who want to gain a deeper understanding of the field.
Provides a comprehensive overview of pattern recognition and machine learning, covering its history, key concepts, and techniques. It good reference for learners who want to gain a deeper understanding of the field.
Provides a comprehensive overview of statistical machine learning, covering its history, key concepts, and techniques. It good reference for learners who want to gain a deeper understanding of the field.
Provides a comprehensive overview of natural language processing, covering its history, key concepts, and techniques. It good reference for learners who want to gain a deeper understanding of the field.
Provides a comprehensive overview of computer vision, covering its history, key concepts, and techniques. It good reference for learners who want to gain a deeper understanding of the field.
Provides a comprehensive overview of robotics, vision, and control, covering its history, key concepts, and techniques. It good reference for learners who want to gain a deeper understanding of the field.
Provides a comprehensive overview of deep learning for natural language processing, covering its history, key concepts, and techniques. It good reference for learners who want to gain a deeper understanding of the field.
Provides a practical introduction to machine learning for people with a programming background. It covers topics such as data preprocessing, model selection, and evaluation.
Provides a concise overview of machine learning, which foundational technology for generative AI. It would be helpful for those who want to quickly get up to speed on the basics.

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