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Google Cloud Training

In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps administrators provision infrastructure. You learn how to prompt Gemini to explain infrastructure, deploy GKE clusters and update existing infrastructure. Using a hands-on lab, you experience how Gemini improves the GKE deployment workflow.

Duet AI was renamed to Gemini, our next-generation model.

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What's inside

Syllabus

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Focuses on improving GKE deployments
Provides hands-on experience in utilizing Gemini
Relevant for cloud architects seeking to enhance their workflow
Taught by Google Cloud Training, recognized for their expertise in cloud computing
May require some foundational knowledge in cloud computing concepts

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Reviews summary

Gemini for cloud architects: an introduction

According to students, 'Gemini for Cloud Architects' offers a largely positive introduction to leveraging generative AI for infrastructure management. Learners appreciate the practical, hands-on labs, finding them effective for understanding how Gemini assists with GKE cluster deployments and infrastructure provisioning. The course is noted as a clear overview and a solid starting point for cloud professionals. However, some indicate the content is introductory and might benefit from deeper dives into advanced use cases for more experienced architects.
Offers a solid foundational understanding of Gemini for cloud architects.
"I got a clear overview of how Gemini can be used efficiently in cloud architecture."
"The course provided a good starting point for understanding Gemini's role in cloud operations."
"It's a great introduction to Gemini for anyone in a cloud operations or architect role."
Course provides invaluable practical experience with Gemini.
"The hands-on lab was extremely useful for deploying GKE clusters and seeing Gemini in action."
"I found the practical demonstrations of Gemini's capabilities very insightful and easy to follow."
"The labs truly helped me understand how Gemini assists with infrastructure provisioning in a real-world context."
Highly relevant and beneficial for cloud architects and administrators.
"This course is highly relevant for my work as a cloud administrator, offering immediate application."
"As a cloud architect, I found the material directly applicable to streamlining daily operations."
"It clearly demonstrates Gemini's value for provisioning and managing infrastructure effectively."
Provides a good overview but might lack advanced, in-depth coverage.
"While a good start, I felt it could go deeper into more complex scenarios and troubleshooting."
"For experienced architects, the content might feel a bit high-level and basic at times."
"I wished for more advanced use cases beyond the basic deployments shown in the labs."

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 Gemini for Cloud Architects with these activities:
Review GKE concepts before starting the course
Brush up on GKE concepts to ensure a smooth start to the course and enhance your ability to understand how Gemini interacts with GKE.
Browse courses on GKE
Show steps
  • Revisit the official GKE documentation website.
  • Review key concepts such as clusters, nodes, and pods.
Read "Site Reliability Engineering" by Betsy Beyer et al.
Build a strong foundation in site reliability engineering principles to complement your understanding of Gemini's role in infrastructure management.
Show steps
  • Purchase or borrow the book from a library or online retailer.
  • Read through the chapters, focusing on the principles and best practices of site reliability engineering.
  • Apply the concepts you learn to your understanding of Gemini.
Join a DevOps community on LinkedIn
Join a community of DevOps professionals to ask questions, share knowledge, and find mentors who can provide guidance and support.
Browse courses on Cloud Computing
Show steps
  • Create a LinkedIn profile that highlights your interest in DevOps.
  • Join relevant DevOps groups on LinkedIn.
  • Introduce yourself to the group and ask for recommendations for mentors.
  • Connect with potential mentors and schedule informational interviews.
Five other activities
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Join a study group focused on Gemini
Engage with peers to discuss and reinforce your understanding of Gemini, exchange ideas, and learn from the perspectives of others.
Browse courses on Gemini
Show steps
  • Post a message on online forums or reach out to classmates to find interested individuals.
  • Establish a regular meeting schedule for the study group.
  • Take turns presenting and discussing Gemini concepts.
Deploy a sample GKE cluster using Gemini
Practice using Gemini to deploy a sample GKE cluster, reinforcing the hands-on lab experience and solidifying your understanding of cluster deployment.
Browse courses on GKE
Show steps
  • Follow the instructions in the lab to set up the sample environment.
  • Use Gemini to create a GKE cluster with the specified configuration.
  • Verify that the cluster is successfully deployed and running.
Attend a workshop on cloud infrastructure management
Participate in a workshop to gain practical experience with cloud infrastructure management tools and techniques, complementing your understanding of Gemini's role in this domain.
Browse courses on Cloud Computing
Show steps
  • Search for upcoming workshops in your area or online.
  • Choose a workshop that aligns with your interests and learning goals.
  • Participate actively in the workshop, asking questions and engaging in discussions.
Explore advanced topics in Gemini documentation
Delve into the Gemini documentation to learn about advanced features and use cases, expanding your knowledge beyond the core concepts covered in the course.
Browse courses on Gemini
Show steps
  • Visit the official Gemini documentation website.
  • Read through the tutorials and articles on advanced topics.
  • Experiment with the code examples provided in the documentation.
Write a blog post on your experience with Gemini
Share your experience with Gemini with the community by creating a blog post, enhancing your understanding and helping others learn about this AI-powered tool.
Browse courses on Gemini
Show steps
  • Write about the hands-on lab you completed in the course.
  • Discuss the challenges you encountered and how you overcame them.
  • Share your insights on the potential of Gemini in the field of infrastructure provisioning.
  • Publish your blog post on a relevant platform.

Career center

Learners who complete Gemini for Cloud Architects will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 50 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 Gemini for Cloud Architects.
Serves as the definitive engineering blueprint for building production applications on multimodal foundation models like Google Gemini, covering long-context prompting, retrieval-augmented generation, agentic tool calling, evaluation pipelines, and latency optimization.
Focuses specifically on Google Kubernetes Engine, covering network, security, monitoring, and automation configurations. It provides hands-on examples for deploying and managing applications on GKE and is highly relevant for gaining a deep understanding of GKE-specific features and best practices. This valuable resource for those working directly with GKE in a professional capacity.
Teaches both developers and business builders how to structure reliable system instructions, few-shot examples, structured JSON outputs, and multimodal chains across Google Gemini and frontier LLM APIs.
Provides a comprehensive guide to cloud architecture patterns, including patterns for designing scalable, resilient, and portable cloud applications. It valuable resource for architects and developers who want to design and build cloud-based systems.
Offers a foundational understanding of Kubernetes, which is essential for grasping GKE. Written by some of the original creators of Kubernetes, it provides clear explanations of core concepts and how the system fits into the lifecycle of distributed applications. It's an excellent starting point for those new to container orchestration and cloud-native patterns, and is often recommended as a primary text for beginners. The latest editions cover updated features and best practices.
Provides a comprehensive overview of cloud computing and big data, covering topics such as cloud computing architectures, big data analytics, and data management. It is an excellent resource for understanding the convergence of cloud computing and big data.
Bridges no-code/low-code citizen development and full-stack software engineering, demonstrating how Google Gemini, GitHub Copilot, and natural-language AI assistants accelerate application prototyping, testing, and deployment.
Considered a comprehensive guide, this book delves into using Kubernetes to deploy container-based distributed applications. It starts with an overview of Docker and Kubernetes before building a cluster, gradually expanding into more advanced topics like monitoring, tuning, and scaling. is highly valuable for gaining a deep, practical understanding of Kubernetes internals and operations, making it a strong follow-up to introductory materials. It is often used as a key reference for practitioners.
Illustrates with visual clarity how modern transformer foundation models represent text and multimodal inputs, perform semantic search, and power intelligent conversational and agentic applications.
Provides a comprehensive overview of cloud computing and software engineering, covering topics such as cloud computing architectures, software development methodologies, and cloud-based software engineering. It is an excellent resource for understanding the relationship between cloud computing and software engineering.
Focuses on applying DevOps principles to cloud-native applications running on Kubernetes. It covers modern best practices for containerized workloads and explores various aspects of the Kubernetes ecosystem from a DevOps perspective. It's highly relevant for understanding how GKE fits into a modern development and operations workflow and is valuable for both developers and operations teams.
Focuses specifically on invoking Google Gemini from Python to generate application code, automate data processing workflows, debug scripts, and prototype AI-powered services rapidly.
Provides a practical guide to using generative AI, covering the different techniques and tools available. It is written by two leading experts in the field, Josh Patterson and Adam Gibson.
Provides a hands-on approach to cloud computing, covering topics such as cloud computing architectures, service models, deployment models, and security considerations. It is an excellent resource for readers who want to learn how to build and deploy cloud-based applications.
Provides a comprehensive overview of cloud computing concepts, technologies, and architectures. It is an excellent resource for understanding the fundamentals of cloud computing and how to design and build cloud-based applications.
Presents a collection of proven patterns for designing and building cloud-native applications. It covers topics such as microservices, containers, and serverless computing. It valuable resource for architects and developers who want to adopt cloud-native practices.
Provides a beginner-friendly introduction to Kubernetes, covering core concepts and architecture. It's updated regularly to stay current with the rapidly evolving cloud-native ecosystem. This book is useful for gaining a broad understanding of Kubernetes fundamentals and good resource for those who prefer a more accessible approach before diving into deeper technical details.
Walks practitioners through architecting low-code and code-first generative AI applications using orchestration frameworks, vector databases, memory buffers, and multimodal foundation model APIs.
Geared towards running Kubernetes in production environments, this book offers insights into key challenges and best practices. It examines the essential building blocks of Kubernetes and considerations for building successful application platforms. While not exclusively about GKE, the production-level knowledge is directly applicable to operating GKE clusters effectively and reliably.
Unpacks the underlying neural architecture of generative foundation models, showing technical readers how attention mechanisms, pretraining, and instruction tuning operate beneath the Gemini API.
Serves as a comprehensive guide to container orchestration with Kubernetes, starting with the fundamentals of containers. It's a good resource for gaining a solid understanding of the underlying technologies that power GKE and is suitable for those with some sysadmin knowledge but new to containers and Kubernetes.
Architects end-to-end generative AI and machine learning workloads on Google Cloud, showing solutions builders how to operationalize Vertex AI, Gemini models, data pipelines, and low-code AutoML services in production.
While a study guide for a specific certification, this book covers the core concepts and administrative tasks required for managing Kubernetes clusters. The knowledge gained is directly applicable to administering GKE clusters and solidifies a broad understanding of Kubernetes operations. It useful reference for anyone preparing for the CKA exam or looking to reinforce their administrative skills.
Equips Python and JavaScript engineers to orchestrate Gemini and frontier language models with LangChain and LangGraph, covering retrieval pipelines, conversational memory, tool calling, and production evaluation.
Focuses on the architectural challenges of building data-intensive applications, such as choosing the right data storage and processing technologies and designing scalable and fault-tolerant systems. It valuable resource for architects and developers who want to build robust and efficient cloud-based applications.
Provides a comprehensive overview of cloud security, covering topics such as cloud security risks, compliance, and best practices. It valuable resource for security professionals who want to protect cloud-based systems.
Provides a practical guide to cloud computing, covering topics such as cloud computing models, deployment strategies, and best practices. It valuable resource for architects and developers who want to design and build cloud-based applications.
Examines how Google's integration of Gemini foundation models into Search and AI Overviews reshapes digital communication, content discovery, brand visibility, and information retrieval strategy.
Similar to the CKA study guide, this book focuses on the skills required for developing applications that run on Kubernetes. Understanding application development on Kubernetes is essential for utilizing GKE effectively. is valuable for developers working with GKE.
Provides the enduring mathematical foundations of neural representation learning, sequence modeling, and gradient optimization that underpin modern multimodal AI systems like Google Gemini.
Connects LangChain orchestration directly with Google Cloud Vertex AI and Gemini APIs, walking Python developers through enterprise retrieval-augmented generation, vector indexing, and multi-step autonomous agents.
Demystifies visual and low-code machine learning on Google Cloud Vertex AI and BigQuery ML, empowering analysts and citizen developers to assemble predictive and generative AI workflows without heavy custom code.
Provides a business-oriented perspective on generative AI, discussing its potential impact on industries and how companies can use it to gain a competitive advantage. It is written by three leading experts in the field, Thomas Davenport, Rajeev Ronanki, and Nitin Mittal.
Focuses on GitOps principles and practices for managing Kubernetes clusters and deploying applications. GitOps popular approach for managing infrastructure and applications on GKE. This book provides practical guidance on implementing continuous deployment workflows using popular GitOps tools.
Provides a thought-provoking exploration of the future of generative AI, discussing its potential benefits and risks. It is written by Gary Marcus, a leading researcher in the field.
Offers an accessible, research-backed framework for non-programmers, educators, and citizen developers collaborating with frontier AI systems like Gemini as creative co-workers and tutors.
Provides a deep dive into serverless architectures, covering topics such as serverless computing models, event-driven programming, and cloud functions. It valuable resource for architects and developers who want to build and deploy serverless applications.
Distills core engineering patterns for reliable AI agents built atop frontier multimodal models like Gemini, covering structured outputs, memory management, tool invocation, routing, and evaluation loops.
This foundational book from Google provides insights into the principles and practices of Site Reliability Engineering (SRE). While not GKE-specific, the SRE principles are highly relevant to operating reliable and scalable systems on platforms like GKE. It offers valuable context on the operational mindset and practices employed by Google, which are integral to the design and management of GKE.
This seminal book on DevOps provides a foundational understanding of the culture, practices, and tools that enable high-performing IT organizations. While not directly about GKE, the principles of DevOps are crucial for successfully implementing and operating GKE in a production environment. It provides essential background knowledge on modern IT operations.
Details how to architect multimodal retrieval-augmented generation pipelines using Google's Gemini Embedding 2 model, indexing text, images, documents, and audio into unified vector spaces for grounded AI synthesis.
A companion to the SRE book, this workbook provides practical examples and case studies for implementing SRE principles. It helps translate the theoretical concepts of SRE into actionable practices, which is beneficial for anyone managing production workloads on GKE and aiming to improve reliability and operational efficiency.
Explores how AI researchers train large foundation models to follow human values, mitigate representation bias, avoid hallucination, and behave safely when deployed to millions of users.
Focuses on infrastructure provisioning with Kubernetes, covering topics such as Kubernetes installation, configuration, and management.
Explores the relationship between generative AI and the creative process, discussing how generative AI can be used to enhance creativity. It is written by Margaret Boden, a leading researcher in the field.
Provides a broad overview of Google Cloud Platform services, including coverage of managing containers with Kubernetes (GKE). While not solely focused on GKE, it offers essential context on the surrounding GCP ecosystem, which is crucial for understanding how GKE integrates with other cloud services. It's a good resource for those new to GCP and wanting to see where GKE fits in.
Offers a visual guide to Google Cloud Platform services, which can be helpful for quickly understanding the landscape where GKE resides. While not a deep dive into GKE, its visual approach can aid in grasping the relationships between different GCP components relevant to GKE deployments. It serves as a useful quick reference.
Explores the potential applications of generative AI in healthcare, discussing how it could be used to improve patient care and accelerate drug discovery. It is written by Eric Topol, a leading researcher in the field.
Focuses on infrastructure provisioning with Vagrant, covering topics such as Vagrant installation, configuration, and management.
Explores the philosophical implications of generative AI, discussing how it challenges our understanding of mind and consciousness. It is written by Daniel C. Dennett, a leading philosopher in the field.

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