Level 1 : Getting Started with GCP
Earned Apr 8, 2024 EDT
Logging and Monitoring in Google Cloud
Earned Apr 4, 2024 EDT
The Arcade Trivia April 2024 Week 1
Earned Apr 4, 2024 EDT
可靠的 Google Cloud 基础设施: 设计和流程
Earned Apr 2, 2024 EDT
The Arcade Trivia March 2024 Week 4
Earned Mar 29, 2024 EDT
优化 Google Kubernetes Engine 的费用
Earned Mar 26, 2024 EDT
Scaling with Google Cloud Operations
Earned Mar 21, 2024 EDT
Google Kubernetes Engine 使用入门
Earned Mar 20, 2024 EDT
为 Compute Engine 实现云负载均衡
Earned Mar 19, 2024 EDT
在 Google Cloud 上设置应用开发环境
Earned Mar 18, 2024 EDT
Google Cloud 弹性基础设施：扩缩和自动化
Earned Mar 18, 2024 EDT
Trust and Security with Google Cloud
Earned Mar 14, 2024 EDT
Google Cloud 重要基础设施：核心服务
Earned Mar 14, 2024 EDT
Google Cloud 重要基础设施：基础
Earned Mar 12, 2024 EDT
Modernize Infrastructure and Applications with Google Cloud
Earned Mar 11, 2024 EDT
Innovating with Google Cloud Artificial Intelligence
Earned Mar 8, 2024 EST
Google Cloud 基础知识：核心基础设施
Earned Mar 7, 2024 EST
Exploring Data Transformation with Google Cloud
Earned Mar 4, 2024 EST
Digital Transformation with Google Cloud
Earned Mar 1, 2024 EST
Preparing for your Professional Cloud Architect Journey
Earned Feb 29, 2024 EST
Serverless Data Processing with Dataflow: Operations
Earned Feb 23, 2024 EST
生成式 AI 简介
Earned Jan 26, 2024 EST
Serverless Data Processing with Dataflow: Develop Pipelines
Earned Sep 8, 2023 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned Aug 8, 2023 EDT
Preparing for your Professional Data Engineer Journey
Earned Aug 8, 2023 EDT
利用 BigQuery ML 构建预测模型时的数据工程处理
Earned Jun 20, 2023 EDT
使用 BigQuery 构建数据仓库
Earned Jun 16, 2023 EDT
在 Google Cloud 上为机器学习 API 准备数据
Earned Jun 15, 2023 EDT
Serverless Data Processing with Dataflow: Foundations
Earned Jun 15, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Jun 13, 2023 EDT
Build Streaming Data Pipelines on Google Cloud
Earned Jun 8, 2023 EDT
Build Batch Data Pipelines on Google Cloud
Earned Jun 5, 2023 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned May 30, 2023 EDT
The course aims to train Google technical sales partners on the business value discovery process using proprietary content.
Course activities use an external tool (Yoodli). Refer to Yoodli's Terms of Service and Privacy Notice.
Note: The Yoodli Labs in this course will be deprecated on March 31st. We encourage you to finish your remaining Yoodli Labs before the March 31 deadline.
“生成式 AI 智能体：助力组织转型”是“Gen AI Leader”学习路线中的第五门课程，也是最后一门课程。本课程探讨了组织如何使用量身定制的生成式 AI 智能体，帮助应对特定的业务挑战。您将亲自动手构建一个基本的生成式 AI 智能体，并探索这些智能体的组成部分，例如模型、推理循环以及各种工具。
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“生成式 AI 应用：改变工作方式”是 Generative AI Leader 学习路线的第四门课程。本课程介绍 Google 的生成式 AI 应用，例如 Gemini for Workspace 和 NotebookLM。它将引导您逐一了解接地、检索增强生成、构建有效提示和构建自动化工作流等概念。
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“生成式 AI: 全面了解生成式 AI”是 Generative AI Leader 学习路线中的第三门课程。生成式 AI 正在改变我们的工作方式,以及我们与周围世界的互动方式。作为领导者,应该如何利用生成式 AI 来推动实现实际的业务成果?在本课程中,您将探索构建生成式 AI 解决方案的不同层级、Google Cloud 的产品,以及选择解决方案时需要考虑的因素。
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“生成式 AI: 剖析基本概念”是 Generative AI Leader 学习路线中的第二门课程。在本课程中,您将了解生成式 AI 的基本概念。您要探索 AI、机器学习和生成式 AI 之间的区别,了解各种数据类型如何赋能生成式 AI,从而应对各种业务挑战。您还将深入了解 Google Cloud 应对基础模型局限性的策略,以及负责任和安全的 AI 开发与部署面临着哪些关键挑战。
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“生成式 AI：不只是聊天机器人”是 Generative AI Leader 学习路线中的第一门课程。学习本课程没有知识门槛。本课程旨在帮助您超越对聊天机器人的基本认知,探索生成式 AI技术为您的组织带来的真正潜力。您将探索基础模型和提示工程等概念,这些知识对利用生成式 AI 的强大功能至关重要。本课程还将说明,为组织制定成功的生成式 AI 策略时,需要考虑哪些重要因素。
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Complete the Configure AI Applications to optimize search results skill badge to demonstrate your proficiency in configuring search results from AI Applications. You will be tasked with implementing search serving controls to boost and bury results, filter entries from search results and display metadata in your search interface.
Please note that AI Applications was previously named Agent Builder, so you may encounter this older name within the lab content.
A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete the assessment challenge lab, to receive a skill badge that you can share with your network.
When you complete this course, you can earn the badge displayed here and claim it on Credly! Boost your cloud career by showing the world the skills you have developed!
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Gemini Enterprise 结合了 Google 在搜索和 AI 领域的专长。它是一款强大的工具，让员工只需通过一个搜索栏，就能从文档库、邮件、聊天消息、工单系统及其他数据源中查找具体信息。Gemini Enterprise 助理还能帮助进行头脑风暴、开展研究、生成文档大纲并执行其他操作，比如邀请同事参加某个日历活动。因此它能加快知识型工作的进度并提升协作效率。（请注意，Gemini Enterprise 以前称为 Google Agentspace，本课程中可能会提及以前的产品名称。）
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This course explores the different products and capabilities of Gemini Enterprise for Customer Experience and Conversational Agents. Additionally, it covers the foundational principles of conversation design to craft engaging and effective experiences that emulate human-like experiences specific to the Chat channel.
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Complete the Create media search and media recommendations applications with AI Applications skill badge to demonstrate your ability to create, configure, and access media search and recommendations applications using AI Applications.
Please note that AI Applications was previously named Agent Builder, so you may encounter this older name within the lab content.
A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete the assessment challenge lab, to receive a skill badge that you can share with your network.
When you complete this course, you can earn the badge displayed here and claim it on Credly! Boost your cloud career by showing the world the skills you have developed!
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Do you want to keep your users engaged by suggesting content they'll love?
This course equips you with the skills to build a cutting-edge recommendations app using your own data with no prior machine learning knowledge. You learn to leverage AI Applications to build recommendation applications so that audiences can discover more personalized content, like what to watch or read next, with Google-quality results customized using optimization objectives.
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Complete the Extend Gemini Enterprise Assistant Capabilities skill badge to demonstrate your ability to extend Gemini Enterprise assistant's capabilities with actions, grounding with Google Search, and a conversational agent.
A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete the assessment challenge lab, to receive a skill badge that you can share with your network.
When you complete this course, you can earn the badge displayed here and claim it on Credly! Boost your cloud career by showing the world the skills you have developed!
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NotebookLM is an AI-powered collaborator that helps you do your best thinking. After uploading your documents, NotebookLM becomes an instant expert in those sources so you can read, take notes, and collaborate with it to refine and organize your ideas.
NotebookLM Pro gives you everything already included with NotebookLM, as well as higher utilization limits, access to premium features, and additional sharing options and analytics.
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If you've worked with data, you know that some data is more reliable than other data.
In this course, you'll learn a variety of techniques to present the most reliable or useful results to your users. Create serving controls to boost or bury search results. Rank search results to ensure that each query is answered by the most relevant data. If needed, tune your search engine. Learn to measure search results to ensure your search applications deliver the best possible results to each user.
(Please note Gemini Enterprise was previously named Google Agentspace, there may be references to the previous product name in this course.)
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AI Applications provides built-in analytics for your Vertex AI Search and Gemini Enterprise apps. Learn what metrics are tracked and how to view them in this course.
(Please note Gemini Enterprise was previously named Google Agentspace, there may be references to the previous product name in this course.)
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Initial deployment of Vertex AI Search and Gemini Enterprise apps takes only a few clicks, but getting the configurations right can elevate a deployment from a basic off-the-shelf app to an excellent custom search or recommendations experience.
In this course, you'll learn more about the many ways you can customize and improve search, recommendations, and Gemini Enterprise apps.
(Please note Gemini Enterprise was previously named Google Agentspace, there may be references to the previous product name in this course.)
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Complete the Build search and recommendations AI Applications skill badge to demonstrate your proficiency in deploying search and recommendation applications through AI Applications. Additionally, emphasis is placed on constructing a tailored Q&A system utilizing data stores.
Please note that AI Applications was previously named Agent Builder, so you may encounter this older name within the lab content.
A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete the assessment challenge lab, to receive a skill badge that you can share with your network.
When you complete this course, you can earn the badge displayed here and claim it on Credly! Boost your cloud career by showing the world the skills you have developed!
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Complete the Create and maintain Vertex AI Search data stores skill badge to demonstrate your proficiency in building various types of data stores used in Vertex AI Search applications.
A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete the assessment challenge lab, to receive a skill badge that you can share with your network.
When you complete this course, you can earn the badge displayed here and claim it on Credly! Boost your cloud career by showing the world the skills you have developed!
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Data stores represent a simple way to make content available to many types of generative AI applications, including search applications, recommendations engines, Gemini Enterprise apps, Agent Development Kit agents, and apps built with Google Gen AI or LangChain SDKs. Connect data from many sources include Cloud Storage, Google Drive, chat apps, mail apps, ticketing systems, third-party file storage providers, Salesforce, and many more.
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This course introduces AI Applications. You will learn about the types of apps that you can create using AI Applications, the high-level steps that its data stores automate for you, and what advanced features can be enabled for Search apps.
(Please note Gemini Enterprise was previously named Google Agentspace, there may be references to the previous product name in this course.)
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此课程将探索如何使用 AI 功能套件 Gemini in BigQuery 为“数据到 AI”工作流提供助力。其中涉及到的功能包括数据探索和准备、代码生成和问题排查，以及工作流发现和可视化。此课程包含概念解释、真实使用场景以及实操实验等内容，可帮助数据从业者提升效率并加快流水线开发速度。
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Cloud SQL is a fully managed database service that stands out from its peers due to high performance, seamless integration, and impressive scalability. In this quest you will receive hands-on practice with the basics of Cloud SQL and quickly progress to advanced features, which you will apply to production frameworks and application environments. From creating instances and querying data with SQL, to building Deployment Manager scripts and connecting Cloud SQL instances with applications run on GKE containers, this quest will give you the knowledge and experience needed so you can start integrating this service right away.
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This course explores the Geographic Information Systems (GIS), GIS Visualization, and machine learning enhancements to BigQuery.
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This course explores how to implement a streaming analytics solution using Dataflow and BigQuery.
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This course explores how to leverage Looker to create data experiences and gain insights with modern business intelligence (BI) and reporting.
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This course explores how to implement a streaming analytics solution using Pub/Sub.
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This course continues to explore the implementation of data load and transformation pipelines for a BigQuery Data Warehouse using Dataflow.
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This course continues to explore the implementation of data load and transformation pipelines for a BigQuery Data Warehouse using Cloud Data Fusion.
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This course identifies best practices for migrating data warehouses to BigQuery and the key skills required to perform successful migration.
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This course explores the implementation of data load and transformation pipelines for a BigQuery Data Warehouse using Dataproc.
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Welcome to Optimize in BigQuery, where we map Enterprise Data Warehouse concepts and components to BigQuery and Google data services with a focus on optimization.
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Welcome to Design in BigQuery, where we map Enterprise Data Warehouse concepts and components to BigQuery and Google data services with a focus on schema design.
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This course discusses the key elements of Google's Data Warehouse solution portfolio and strategy.
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这是一节入门级微学习课程，探讨什么是大型语言模型 (LLM)、适合的应用场景以及如何使用提示调整来提升 LLM 性能，还介绍了可以帮助您开发自己的 Gen AI 应用的各种 Google 工具。
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Earn a skill badge by passing the final quiz, you'll demonstrate your understanding of foundational concepts in generative AI.
A skill badge is a digital badge issued by Google Cloud in recognition of your knowledge of Google Cloud products and services. Share your skill badge by making your profile public and adding it to your social media profile.
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Text Prompt Engineering Techniques introduces you to consider different strategic approaches & techniques to deploy when writing prompts for text-based generative AI tasks.
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本课程介绍 Vertex AI Studio，这是一种用于与生成式 AI 模型交互、围绕业务创意进行原型设计并在生产环境中落地的工具。通过沉浸式应用场景、富有吸引力的课程和实操实验，您将探索从提示到产品的整个生命周期，了解如何将 Vertex AI Studio 用于多模态 Gemini 应用、提示设计、提示工程和模型调优。本课程的目的在于帮助您利用 Vertex AI Studio，在自己的项目中充分发掘生成式 AI 的潜力。
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完成 在 Vertex AI 中设计提示入门技能徽章课程，展示以下方面的技能： Vertex AI 中的提示工程、图片分析和多模态生成式技术。探索如何编写有效的提示，指导生成式 AI 输出， 以及将 Gemini 模型应用于真实的营销场景。
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随着企业对人工智能和机器学习的应用越来越广泛，以负责任的方式构建这些技术也变得更加重要。但对很多企业而言，真正践行 Responsible AI 并非易事。如果您有意了解如何在组织内践行 Responsible AI，本课程正适合您。
本课程将介绍 Google Cloud 目前如何践行 Responsible AI，以及从中总结的最佳实践和经验教训，便于您以此为框架构建自己的 Responsible AI 方法。
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这是一节入门级微课程，旨在解释什么是负责任的 AI、它的重要性，以及 Google 如何在自己的产品中实现负责任的 AI。此外，本课程还介绍了 Google 的 7 个 AI 开发原则。
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A Business Leader in Generative AI can articulate the capabilities of core cloud Generative AI products and services and understand how they benefit organizations. This course provides an overview of the types of opportunities and challenges that companies often encounter in their digital transformation journey and how they can leverage Google Cloud's generative AI products to overcome these challenges.
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完成设置 Google Cloud 网络课程，赢取技能徽章， 您将了解如何在 Google Cloud Platform 上执行基本的网络组建和管理任务 - 创建自定义网络、添加子网防火墙规则，然后创建虚拟机并测试 虚拟机之间相互通信时的延迟时间。
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完成在 Google Cloud 上使用 Terraform 构建基础设施技能徽章中级课程， 展示您在以下方面的技能：在使用 Terraform 时遵循基础设施即代码 (IaC) 原则；利用 Terraform 配置 来预配和管理 Google Cloud 资源；管理有效状态（本地和远程）；以及将 Terraform 代码模块化，以方便重复使用和整理。
Businesses are flocking to the cloud, and the demand for skilled professionals is skyrocketing. Join the trend and level up your resume with in-demand skills and earn a Google Cloud Credential to showcase your expertise. No experience necessary – get started today!
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Welcome to the two-part course on Logging, Monitoring, and Observability in Google Cloud. The core operations tools in Google Cloud break down into two major categories. The operations-focused components and the application performance management tools. This course, Logging and Monitoring in Google Cloud, covers the operations-focused components including Logging, Monitoring, and Service Monitoring. After taking this course, it is suggested that you complete part 2, Observability in Google Cloud, to learn about the available application performance management tools.
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Hey there! You're invited to game on with the Arcade Trivia for April Week 1! Play throughout the month and boost your cloud learning journey. Every week, we'll release a new set of questions to test your knowledge of Google Cloud Platform. Get started now and earn the April Trivia Week 1 badge!
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本课程指导学员运用久经考验的设计模式在 Google Cloud 上构建高度可靠且高效的解决方案。它是“Google Compute Engine 架构设计”或“Google Kubernetes Engine 架构设计”课程的延续，并假定您有使用其中任何一门课程所涵盖技术的实践经验。通过一系列演示、设计活动和动手实验，学员可以了解如何定义及平衡业务要求和技术要求，以便设计可靠性和可用性高、安全且经济实惠的 Google Cloud 部署。
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Hey there! You're invited to game on with the Arcade Trivia for March Week 4! Play throughout the month and boost your cloud learning journey. Every week, we'll release a new set of questions to test your knowledge of Google Cloud Platform. Get started now and earn the March Trivia Week 4 badge!
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完成中级技能徽章课程“优化 Google Kubernetes Engine 的费用”， 展示您在以下方面的技能：创建和管理多租户集群、按命名空间监控资源使用情况、 配置集群和 Pod 自动扩缩以提高效率、设置负载均衡以实现最佳资源分布， 以及实现活跃性与就绪性探测以确保应用正常运行并具有成本效益。
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Organizations of all sizes are embracing the power and flexibility of the cloud to transform how they operate. However, managing and scaling cloud resources effectively can be a complex task.
Scaling with Google Cloud Operations explores the fundamental concepts of modern operations, reliability, and resilience in the cloud, and how Google Cloud can help support these efforts.
Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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欢迎学习“Google Kubernetes Engine 使用入门”课程。Kubernetes 是位于应用和硬件基础架构之间的软件层，如果您对 Kubernetes 感兴趣，那就来对地方了！Google Kubernetes Engine 将 Kubernetes 作为 Google Cloud 上的代管式服务提供给您使用。
本课程的目标是介绍 Google Kubernetes Engine（通常称为 GKE）的基础知识，以及将应用容器化并在 Google Cloud 中运行的方法。本课程首先介绍 Google Cloud 的基础知识，然后概述容器、Kubernetes、Kubernetes 架构以及 Kubernetes 操作。
完成“在 Google Cloud 上设置应用开发环境”课程，赢取技能徽章；通过该课程，您将了解如何使用以下技术的基本功能来构建和连接以存储为中心的云基础设施： Cloud Storage、Identity and Access Management、Cloud Functions 和 Pub/Sub。
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这是一套自助式速成课程，向学员介绍 Google Cloud 提供的灵活全面的基础架构和平台服务。学员将通过一系列视频讲座、演示和实操实验，探索和部署各种解决方案元素，包括安全互连网络、负载均衡、自动扩缩、基础架构自动化和代管式服务。
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As organizations move their data and applications to the cloud, they must address new security challenges. The Trust and Security with Google Cloud course explores the basics of cloud security, the value of Google Cloud's multilayered approach to infrastructure security, and how Google earns and maintains customer trust in the cloud.
Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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这门自助式速成课程向学员介绍 Google Cloud 提供的灵活全面的基础架构和平台服务，着重介绍了 Compute Engine。学员将通过一系列视频讲座、演示和动手实验，探索和部署各种解决方案元素，包括网络、系统和应用服务等基础架构组件。本课程的内容还包括如何部署实用的解决方案，包括客户提供的加密密钥、安全和访问权限管理、配额和结算，以及资源监控。
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这门自助式速成课程向学员介绍 Google Cloud 提供的灵活全面的基础架构和平台服务，其中着重介绍了 Compute Engine。学员将通过一系列视频讲座、演示和动手实验，探索和部署各种解决方案元素，包括网络、虚拟机和应用服务等基础架构组件。您将学习如何通过控制台和 Cloud Shell 使用 Google Cloud。您还将了解云架构师角色、基础架构设计方法以及虚拟网络配置和虚拟私有云 (VPC)、项目、网络、子网、IP 地址、路由及防火墙规则。
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Many traditional enterprises use legacy systems and applications that can't stay up-to-date with modern customer expectations. Business leaders often have to choose between maintaining their aging IT systems or investing in new products and services. "Modernize Infrastructure and Applications with Google Cloud" explores these challenges and offers solutions to overcome them by using cloud technology.
Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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Artificial intelligence (AI) and machine learning (ML) represent an important evolution in information technologies that are quickly transforming a wide range of industries. “Innovating with Google Cloud Artificial Intelligence” explores how organizations can use AI and ML to transform their business processes.
Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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“Google Cloud 基础知识：核心基础设施”介绍在使用 Google Cloud 时会遇到的重要概念和术语。本课程通过视频和实操实验来介绍并比较 Google Cloud 的多种计算和存储服务，并提供重要的资源和政策管理工具。
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Cloud technology can bring great value to an organization, and combining the power of cloud technology with data has the potential to unlock even more value and create new customer experiences.
“Exploring Data Transformation with Google Cloud” explores the value data can bring to an organization and ways Google Cloud can make data useful and accessible.
Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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There's much excitement about cloud technology and digital transformation, but often many unanswered questions.
For example: What is cloud technology? What does digital transformation mean? How can cloud technology help your organization? Where do you even begin?
If you've asked yourself any of these questions, you're in the right place. This course provides an overview of the types of opportunities and challenges that companies often encounter in their digital transformation journey. If you want to learn about cloud technology so you can excel in your role and help build the future of your business, then this introductory course on digital transformation is for you. This course is part of the Cloud Digital Leader learning path.
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This course helps learners create a study plan for the PCA (Professional Cloud Architect) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.
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In the last installment of the Dataflow course series, we will introduce the components of the Dataflow operational model. We will examine tools and techniques for troubleshooting and optimizing pipeline performance. We will then review testing, deployment, and reliability best practices for Dataflow pipelines. We will conclude with a review of Templates, which makes it easy to scale Dataflow pipelines to organizations with hundreds of users. These lessons will help ensure that your data platform is stable and resilient to unanticipated circumstances.
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这是一节入门级微课程，旨在解释什么是生成式 AI、它的用途以及与传统机器学习方法的区别。该课程还介绍了可以帮助您开发自己的生成式 AI 应用的各种 Google 工具。
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In this second installment of the Dataflow course series, we are going to be diving deeper on developing pipelines using the Beam SDK. We start with a review of Apache Beam concepts. Next, we discuss processing streaming data using windows, watermarks and triggers. We then cover options for sources and sinks in your pipelines, schemas to express your structured data, and how to do stateful transformations using State and Timer APIs. We move onto reviewing best practices that help maximize your pipeline performance. Towards the end of the course, we introduce SQL and Dataframes to represent your business logic in Beam and how to iteratively develop pipelines using Beam notebooks.
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This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.
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This course helps learners create a study plan for the PDE (Professional Data Engineer) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.
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完成中级技能徽章课程利用 BigQuery ML 构建预测模型时的数据工程处理， 展示自己在以下方面的技能：利用 Dataprep by Trifacta 构建 BigQuery 数据转换流水线； 利用 Cloud Storage、Dataflow 和 BigQuery 构建提取、转换和加载 (ETL) 工作流； 以及利用 BigQuery ML 构建机器学习模型。
完成入门级技能徽章课程在 Google Cloud 上为机器学习 API 准备数据，展示以下技能： 使用 Dataprep by Trifacta 清理数据、在 Dataflow 中运行数据流水线、在 Dataproc 中创建集群和运行 Apache Spark 作业，以及调用机器学习 API，包括 Cloud Natural Language API、Google Cloud Speech-to-Text API 和 Video Intelligence API。
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This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next, we talk about the Apache Beam vision and the benefits of the Beam Portability framework. The Beam Portability framework achieves the vision that a developer can use their favorite programming language with their preferred execution backend. We then show you how Dataflow allows you to separate compute and storage while saving money, and how identity, access, and management tools interact with your Dataflow pipelines. Lastly, we look at how to implement the right security model for your use case on Dataflow.
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Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.
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In this course you will get hands-on in order to work through real-world challenges faced when building streaming data pipelines. The primary focus is on managing continuous, unbounded data with Google Cloud products.
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In this intermediate course, you will learn to design, build, and optimize robust batch data pipelines on Google Cloud. Moving beyond fundamental data handling, you will explore large-scale data transformations and efficient workflow orchestration, essential for timely business intelligence and critical reporting.
Get hands-on practice using Dataflow for Apache Beam and Serverless for Apache Spark (Dataproc Serverless) for implementation, and tackle crucial considerations for data quality, monitoring, and alerting to ensure pipeline reliability and operational excellence. A basic knowledge of data warehousing, ETL/ELT, SQL, Python, and Google Cloud concepts is recommended.
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While the traditional approaches of using data lakes and data warehouses can be effective, they have shortcomings, particularly in large enterprise environments. This course introduces the concept of a data lakehouse and the Google Cloud products used to create one. A lakehouse architecture uses open-standard data sources and combines the best features of data lakes and data warehouses, which addresses many of their shortcomings.
Discover Business Value for Customers
Earned Dec 15, 2025 EST
在 BigQuery 中使用 Gemini 模型
Earned Nov 12, 2025 EST
生成式 AI 智能体：助力组织转型
Earned Nov 10, 2025 EST
生成式 AI 应用：改变工作方式
Earned Oct 14, 2025 EDT
生成式 AI: 全面了解生成式 AI
Earned Oct 14, 2025 EDT
生成式 AI：剖析基本概念
Earned Oct 9, 2025 EDT
生成式 AI：不只是聊天机器人
Earned Oct 9, 2025 EDT
Configure AI Applications to optimize search results
Earned Sep 4, 2025 EDT
借助 Gemini Enterprise 加速知识交流
Earned Sep 4, 2025 EDT
Introduction to Gemini Enterprise for Customer Experience and Conversational Agents
Earned Aug 11, 2025 EDT
Create media search and media recommendations applications with AI Applications
Earned Aug 6, 2025 EDT
Recommendations with AI Applications
Earned Aug 6, 2025 EDT
Extend Gemini Enterprise Assistant Capabilities
Earned Aug 5, 2025 EDT
Introduction to NotebookLM
Earned Aug 5, 2025 EDT
Improve Vertex AI Search and Gemini Enterprise Search Results
Earned Aug 1, 2025 EDT
Vertex AI Search and Gemini Enterprise Analytics
Earned Aug 1, 2025 EDT
Vertex AI Search and Gemini Enterprise UI Configurations
Earned Aug 1, 2025 EDT
Build search and recommendations applications with AI Applications
Earned Aug 1, 2025 EDT
Create and maintain Vertex AI Search data stores
Earned Jul 31, 2025 EDT
Create Data Stores for Gen AI Applications
Earned Jul 31, 2025 EDT
Introduction to AI Applications
Earned Jul 30, 2025 EDT
使用 Gemini in BigQuery 提高效率
Earned Jul 29, 2025 EDT
Cloud SQL
Earned May 7, 2025 EDT
Data Warehousing for Partners: BigQuery Extended Capabilities
Earned Apr 10, 2025 EDT
Data Warehousing for Partners: Streaming Analytics
Earned Apr 10, 2025 EDT
Data Warehousing for Partners: Analyze Data with Looker
Earned Apr 10, 2025 EDT
Data Warehousing for Partners: Stream Data with Pub/Sub
Earned Apr 8, 2025 EDT
Data Warehousing for Partners: Process Data with Dataflow
Earned Apr 8, 2025 EDT
Data Warehousing for Partners: Cloud Data Fusion Pipelines
Earned Apr 4, 2025 EDT
Data Warehousing for Partners: Migrate Data to BigQuery
Earned Apr 4, 2025 EDT
Data Warehousing for Partners: Process Data with Dataproc
Earned Apr 3, 2025 EDT
Data Warehousing for Partners: Optimize in BigQuery
Earned Apr 3, 2025 EDT
Data Warehousing for Partners: Design in BigQuery
Earned Apr 3, 2025 EDT
Data Warehousing for Partners: Enable Google Cloud Customers
Earned Apr 2, 2025 EDT
大型语言模型简介
Earned Jul 29, 2024 EDT
Generative AI Fundamentals
Earned Jul 26, 2024 EDT
Text Prompt Engineering Techniques
Earned Jul 25, 2024 EDT
Vertex AI Studio 简介
Earned Jul 24, 2024 EDT
在 Vertex AI 中设计提示
Earned Jul 24, 2024 EDT
Responsible AI: 和 Google Cloud 一起践行 AI 原则
Earned Jul 23, 2024 EDT
负责任的 AI 简介
Earned Jul 23, 2024 EDT
Generative AI for Business Leaders
Earned Jul 23, 2024 EDT
设置 Google Cloud 网络
Earned Apr 16, 2024 EDT
在 Google Cloud 上使用 Terraform 构建基础设施
Earned Apr 10, 2024 EDT
开发 Google Cloud 网络
Earned Apr 9, 2024 EDT
Level 1 : Getting Started with GCP
Earned Apr 8, 2024 EDT
Logging and Monitoring in Google Cloud
Earned Apr 4, 2024 EDT
The Arcade Trivia April 2024 Week 1
Earned Apr 4, 2024 EDT
可靠的 Google Cloud 基础设施: 设计和流程
Earned Apr 2, 2024 EDT
The Arcade Trivia March 2024 Week 4
Earned Mar 29, 2024 EDT
优化 Google Kubernetes Engine 的费用
Earned Mar 26, 2024 EDT
Scaling with Google Cloud Operations
Earned Mar 21, 2024 EDT
Google Kubernetes Engine 使用入门
Earned Mar 20, 2024 EDT
为 Compute Engine 实现云负载均衡
Earned Mar 19, 2024 EDT
在 Google Cloud 上设置应用开发环境
Earned Mar 18, 2024 EDT
Google Cloud 弹性基础设施：扩缩和自动化
Earned Mar 18, 2024 EDT
Trust and Security with Google Cloud
Earned Mar 14, 2024 EDT
Google Cloud 重要基础设施：核心服务
Earned Mar 14, 2024 EDT
Google Cloud 重要基础设施：基础
Earned Mar 12, 2024 EDT
Modernize Infrastructure and Applications with Google Cloud
Earned Mar 11, 2024 EDT
Innovating with Google Cloud Artificial Intelligence
Earned Mar 8, 2024 EST
Google Cloud 基础知识：核心基础设施
Earned Mar 7, 2024 EST
Exploring Data Transformation with Google Cloud
Earned Mar 4, 2024 EST
Digital Transformation with Google Cloud
Earned Mar 1, 2024 EST
Preparing for your Professional Cloud Architect Journey
Earned Feb 29, 2024 EST
Serverless Data Processing with Dataflow: Operations
Earned Feb 23, 2024 EST
生成式 AI 简介
Earned Jan 26, 2024 EST
Serverless Data Processing with Dataflow: Develop Pipelines
Earned Sep 8, 2023 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned Aug 8, 2023 EDT
Preparing for your Professional Data Engineer Journey
Earned Aug 8, 2023 EDT
利用 BigQuery ML 构建预测模型时的数据工程处理
Earned Jun 20, 2023 EDT
使用 BigQuery 构建数据仓库
Earned Jun 16, 2023 EDT
在 Google Cloud 上为机器学习 API 准备数据
Earned Jun 15, 2023 EDT
Serverless Data Processing with Dataflow: Foundations
Earned Jun 15, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Jun 13, 2023 EDT
Build Streaming Data Pipelines on Google Cloud
Earned Jun 8, 2023 EDT
Build Batch Data Pipelines on Google Cloud
Earned Jun 5, 2023 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned May 30, 2023 EDT