Google DeepMind: Train A Small Language Model
Earned Jan 11, 2026 EST
安全性與身分識別基礎知識
Earned Jan 6, 2026 EST
透過 Google Cloud Observability 監控及記錄系統狀態
Earned Jan 4, 2026 EST
Logging and Monitoring in Google Cloud
Earned Jan 4, 2026 EST
在 Google Cloud 環境導入 DevSecOps 做法
Earned Jan 3, 2026 EST
Cloud Logging
Earned Jan 1, 2026 EST
Machine Learning Operations (MLOps): Getting Started
Earned Dec 29, 2025 EST
Google Kubernetes Engine Best Practices: Security
Earned Dec 29, 2025 EST
Google Cloud 中的 Kubernetes
Earned Dec 29, 2025 EST
Getting Started with Terraform for Google Cloud
Earned Dec 29, 2025 EST
Hybrid Cloud Service Mesh with Anthos
Earned Dec 27, 2025 EST
可靠的 Google Cloud 基礎架構：設計與程序
Earned Dec 25, 2025 EST
生成式 AI：不只是聊天機器人
Earned Oct 6, 2025 EDT
Developing a Google SRE Culture
Earned Sep 24, 2025 EDT
生成式 AI 適用的機器學習運作 (MLOps)
Earned Sep 11, 2025 EDT
負責任的 AI 技術簡介
Earned Sep 4, 2025 EDT
大型語言模型簡介
Earned Sep 4, 2025 EDT
Deutsche Bank: Prompt Engineering Fundamentals
Earned Sep 3, 2025 EDT
DEPRECATED Cloud Architecture
Earned Mar 17, 2023 EDT
在 Google Cloud 使用 Terraform 建構基礎架構
Earned Jan 19, 2023 EST
在 Google Cloud 部署 Kubernetes 應用程式
Earned Jan 12, 2023 EST
建立 Google Cloud 網路
Earned Dec 30, 2022 EST
Using the Cloud SDK Command Line
Earned Dec 29, 2022 EST
在 Compute Engine 導入 Cloud Load Balancing
Earned Dec 27, 2022 EST
在 Google Cloud 實作 Cloud 安全防護措施：基礎知識
Earned Dec 15, 2022 EST
建構安全的 Google Cloud 網路
Earned Dec 12, 2022 EST
在 Google Cloud 設定應用程式開發環境
Earned Nov 3, 2022 EDT
Google Cloud 必備知識
Earned Nov 2, 2022 EDT
本課程介紹的 Gemini 是採用生成式 AI 技術的協作工具，可協助您透過 Google Cloud 使用 Google 產品和服務，開發、測試、部署及管理應用程式。有了 Gemini 的協助，您會學到如何開發和建構網頁應用程式、修正應用程式中的錯誤、開發測試及查詢資料。在實作研究室中，您也會體驗到 Gemini 如何改良軟體開發生命週期 (SDLC)。
Duet AI 已更名為 Gemini，這是我們的新一代模型。
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本課程介紹的 Gemini 是採用生成式 AI 技術的協作工具，可協助開發人員透過 Google Cloud 建構應用程式。您將瞭解如何透過提示讓 Gemini 為您解釋程式碼內容、推薦 Google Cloud 服務，以及生成應用程式的程式碼。在實作研究室中，您也會體驗到 Gemini 如何改良應用程式的開發工作流程。
Duet AI 已更名為 Gemini，這是我們的新一代模型。
本課程將介紹擴散模型，這是一種機器學習模型，近期在圖像生成領域展現亮眼潛力。概念源自物理學，尤其深受熱力學影響。過去幾年來，在學術界和業界都是炙手可熱的焦點。在 Google Cloud 中，擴散模型是許多先進圖像生成模型和工具的基礎。課程將介紹擴散模型背後的理論，並說明如何在 Vertex AI 上訓練和部署這些模型。
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本課程介紹 Gemini Enterprise，這個強大的平台結合 AI 代理、企業搜尋工具、NotebookLM 和智慧資料存取功能，可協助組織解決難題。學員將能透過實際案例和練習，瞭解 Gemini Enterprise 功能如何滿足實際業務需求、描述平台架構，並說明如何根據不同職務處理資料存取與隱私權事宜。
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本課程提供 Google Cloud 代理平台的完整說明，包括 Vertex AI Agent Builder、Gemini Enterprise、Conversational Agents，以及 Agent Development Kit。學員將瞭解每項產品的獨特功能，區分各特定用途適用的最佳解決方案，並掌握建立搜尋和即時通訊應用程式的基礎知識。
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本課程介紹的 Gemini 是採用生成式 AI 技術的協作工具，可協助分析客戶資料及預測產品銷售情形。您也會學習如何在 BigQuery 中使用客戶資料識別、分類及開發新客戶。透過使用實作研究室，您可以體驗 Gemini 如何改良資料分析和機器學習工作流程。
Duet AI 已更名為 Gemini，這是我們的新一代模型。
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這個入門微學習課程主要說明生成式 AI 的定義和使用方式，以及此 AI 與傳統機器學習方法的差異。本課程也會介紹各項 Google 工具，協助您開發自己的生成式 AI 應用程式。
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Complete the advanced Google DeepMind: Train A Small Language Model skill badge by completing this course to demonstrate skills in the following: formulating real-world language model research problems; building a simple tokenizer; preparing a dataset for training a transformer language model; running the training loop of a small language model. Access this lab at no-cost by signing up for the no-cost subscription. Receive 35 free credits each month!
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Google Cloud 的服務在安全上絕不妥協， 因此開發了專用工具，確保所有專案安全無虞， 使用者也能妥善管理身分識別機制。在這堂入門課程中，您會實際使用 Google Cloud 的 Identity and Access Management (IAM) 服務， 練習管理使用者和虛擬機器帳戶。您將 佈建虛擬私有雲和 VPN 來熟悉網路安全功能，並瞭解有哪些工具 可防範資安威脅和資料遺失。
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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這堂課程會介紹基本技巧，以便您在 Google Cloud 採用安全又有效率的 DevSecOps 做法。您會瞭解如何使用 Artifact Registry、 Cloud Build、Cloud Deploy 和二進位授權等 Google Cloud 服務來保護開發管道，在整個 CI/CD 管道建構、測試及部署採用安全控管機制的容器化應用程式。
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Cloud Logging is a fully managed service that performs at scale. It can ingest application and system log data from thousands of VMs and, even better, analyze all that log data in real time. In this fundamental-level Quest, you learn how to store, search, analyze, monitor, and alert on log data and events from Google Cloud. The labs in the Quest give you hands-on practice using Cloud Logging to maximize your learning experience and provide insight on how you can use Cloud Logging to your own Google Cloud environment.
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This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.
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Get Anthos Ready. This Google Kubernetes Engine-centric quest of best practice hands-on labs focuses on security at scale when deploying and managing production GKE environments -- specifically role-based access control, hardening, VPC networking, and binary authorization.
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Kubernetes 是最受歡迎的容器自動化調度管理系統，Google Kubernetes Engine 則專門支援 Google Cloud 中的 代管 Kubernetes 部署項目。這門進階課程將帶您實際練習設定 Docker 映像檔和容器，並部署完整的 Kubernetes Engine 應用程式。 您會學到如何將容器自動化調度管理機制， 整合到自己的工作流程，這些技巧相當實用。 想透過實作挑戰實驗室展現 技能、驗收學習成果嗎？本課程結束後，再完成 在 Google Cloud 部署 Kubernetes 應用程式課程 結尾的挑戰實驗室，即可獲得專屬 Google Cloud 數位徽章。
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This course provides an introduction to using Terraform for Google Cloud. It enables learners to describe how Terraform can be used to implement infrastructure as code and to apply some of its key features and functionalities to create and manage Google Cloud infrastructure.
Learners will get hands-on practice building and managing Google Cloud resources using Terraform.
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This on-demand course equips students to understand and adopt Istio-based
service-mesh with Anthos for centralized observability, traffic management,
and service-level security.
This is the second course of the Architecting Hybrid Cloud Infrastructure with
Anthos series. After completing this course, learners should continue to the
Hybrid Cloud Multi-Cluster with Anthos course.
Completion of the Architecting with Google Kubernetes Engine path is a
prerequisite for this course.
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這堂課程可讓參加人員瞭解如何使用確實有效的設計模式，在 Google Cloud 中打造相當可靠且效率卓越的解決方案。這堂課程接續了「設定 Google Compute Engine 架構」或「設定 Google Kubernetes Engine 架構」課程的內容，並假設參加人員曾實際運用上述任一課程涵蓋的技術。這堂課程結合了簡報、設計活動和實作研究室，可讓參加人員瞭解如何定義業務和技術需求，並在兩者之間取得平衡，設計出相當可靠、可用性高、安全又符合成本效益的 Google Cloud 部署項目。
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「生成式 AI：不只是聊天機器人」是 Generative AI Leader 學習路徑的第一門課程,沒有任何修課條件。本課程將帶您超越基本知識,進一步瞭解聊天機器人,探索如何在組織中充分發揮生成式 AI 的潛力。您將瞭解基礎模型和提示工程等概念,掌握善用生成式AI 的關鍵。本課程也會帶您瞭解擬定生成式 AI 策略時的多種重要考量,協助您為組織擬定出成功的策略。
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In many IT organizations, incentives are not aligned between developers, who strive for agility, and operators, who focus on stability. Site reliability engineering, or SRE, is how Google aligns incentives between development and operations and does mission-critical production support. Adoption of SRE cultural and technical practices can help improve collaboration between the business and IT. This course introduces key practices of Google SRE and the important role IT and business leaders play in the success of SRE organizational adoption.
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本課程旨在提供必要的知識和工具，協助您探索機器學習運作團隊在部署及管理生成式 AI 模型時面臨的獨特挑戰，並瞭解 Vertex AI 如何幫 AI 團隊簡化機器學習運作程序，打造成效非凡的生成式 AI 專案。
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這個入門微學習課程主要介紹「負責任的 AI 技術」和其重要性，以及 Google 如何在自家產品中導入這項技術。本課程也會說明 Google 的 7 個 AI 開發原則。
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這是一堂入門級的微學習課程，旨在探討大型語言模型 (LLM) 的定義和用途，並說明如何調整提示來提高 LLM 成效。此外，也會介紹多項 Google 工具，協助您自行開發生成式 AI 應用程式。
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Master your Gen AI skills through hands-on labs!
Embark on a dynamic learning experience with Prompt Engineering Fundamentals, where you'll learn the art of designing effective prompts that harness the full power of Generative AI. Dive into the essentials of crafting clear and compelling prompts while exploring advanced techniques such as few-shot and Free-form prompting. Through hands-on labs, you'll apply your newfound skills to creatively analyze images, design prompts, and generate conversations directly on the Google Cloud console.
Upon completion, you'll earn a digital badge showcasing your newly acquired skills, perfect for highlighting on your LinkedIn profile and email signature. Remember to click "End" after finishing each lab to claim your well-deserved points.
This course is OPTIONAL, nevertheless HIGHLY recommended.
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This fundamental-level quest is unique amongst the other quest offerings. The labs have been curated to give IT professionals hands-on practice with topics and services that appear in the Google Cloud Certified Professional Cloud Architect Certification. From IAM, to networking, to Kubernetes engine deployment, this quest is composed of specific labs that will put your Google Cloud knowledge to the test. Be aware that while practice with these labs will increase your skills and abilities, we recommend that you also review the exam guide and other available preparation resources.
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完成「在 Google Cloud 使用 Terraform 建構基礎架構」技能徽章中階課程， 即可證明自己具備下列知識與技能：使用 Terraform 的基礎架構即程式碼 (IaC) 原則、運用 Terraform 設定佈建及管理 Google Cloud 資源、有效管理狀態 (本機和遠端)，以及將 Terraform 程式碼模組化，以利重複使用和管理。
For everyone using Google Cloud Platform for the first time, getting familar with gcloud, Google Cloud's command line, will help you get up to speed faster. In this quest, you'll learn how to install and configure Cloud SDK, then use gcloud to perform some basic operations like creating VMs, networks, using BigQuery, and using gsutil to perform operations.
使用 Gemini：端對端 SDLC
Earned Apr 13, 2026 EDT
使用 Gemini：應用程式開發人員
Earned Apr 3, 2026 EDT
編碼器-解碼器架構
Earned Jan 29, 2026 EST
注意力機制
Earned Jan 29, 2026 EST
圖像生成簡介
Earned Jan 29, 2026 EST
Gemini Enterprise 簡介
Earned Jan 28, 2026 EST
瞭解 Google Cloud 代理
Earned Jan 28, 2026 EST
使用 Gemini：數據資料學家和分析師
Earned Jan 18, 2026 EST
生成式 AI 簡介
Earned Jan 18, 2026 EST
Google DeepMind: Train A Small Language Model
Earned Jan 11, 2026 EST
安全性與身分識別基礎知識
Earned Jan 6, 2026 EST
透過 Google Cloud Observability 監控及記錄系統狀態
Earned Jan 4, 2026 EST
Logging and Monitoring in Google Cloud
Earned Jan 4, 2026 EST
在 Google Cloud 環境導入 DevSecOps 做法
Earned Jan 3, 2026 EST
Cloud Logging
Earned Jan 1, 2026 EST
Machine Learning Operations (MLOps): Getting Started
Earned Dec 29, 2025 EST
Google Kubernetes Engine Best Practices: Security
Earned Dec 29, 2025 EST
Google Cloud 中的 Kubernetes
Earned Dec 29, 2025 EST
Getting Started with Terraform for Google Cloud
Earned Dec 29, 2025 EST
Hybrid Cloud Service Mesh with Anthos
Earned Dec 27, 2025 EST
可靠的 Google Cloud 基礎架構：設計與程序
Earned Dec 25, 2025 EST
生成式 AI：不只是聊天機器人
Earned Oct 6, 2025 EDT
Developing a Google SRE Culture
Earned Sep 24, 2025 EDT
生成式 AI 適用的機器學習運作 (MLOps)
Earned Sep 11, 2025 EDT
負責任的 AI 技術簡介
Earned Sep 4, 2025 EDT
大型語言模型簡介
Earned Sep 4, 2025 EDT
Deutsche Bank: Prompt Engineering Fundamentals
Earned Sep 3, 2025 EDT
DEPRECATED Cloud Architecture
Earned Mar 17, 2023 EDT
在 Google Cloud 使用 Terraform 建構基礎架構
Earned Jan 19, 2023 EST
在 Google Cloud 部署 Kubernetes 應用程式
Earned Jan 12, 2023 EST
建立 Google Cloud 網路
Earned Dec 30, 2022 EST
Using the Cloud SDK Command Line
Earned Dec 29, 2022 EST
在 Compute Engine 導入 Cloud Load Balancing
Earned Dec 27, 2022 EST
在 Google Cloud 實作 Cloud 安全防護措施：基礎知識
Earned Dec 15, 2022 EST
建構安全的 Google Cloud 網路
Earned Dec 12, 2022 EST
在 Google Cloud 設定應用程式開發環境
Earned Nov 3, 2022 EDT
Google Cloud 必備知識
Earned Nov 2, 2022 EDT