Mark Johnson
Member since 2018
Silver League
39507 points
為 Google Cloud 設定服務帳戶和 IAM 角色
Earned Oct 27, 2026 EDT
在 Google Cloud 部署 Kubernetes 應用程式
Earned Oct 27, 2026 EDT
在 Google Cloud 使用 Terraform 建構基礎架構
Earned Jul 12, 2026 EDT
在 Google Cloud 實作 Cloud 安全防護措施：基礎知識
Earned Jul 12, 2026 EDT
生成式 AI：瞭解基礎概念
Earned Oct 23, 2025 EDT
[DEPRECATED] Introduction to NotebookLM
Earned Aug 14, 2025 EDT
Introduction to AI Applications
Earned Aug 14, 2025 EDT
Create Data Stores for Gen AI Applications
Earned Aug 14, 2025 EDT
Evaluate Gen AI model and agent performance
Earned Aug 13, 2025 EDT
Implement Hybrid Search
Earned Aug 13, 2025 EDT
Implement RAG with Agent Platform
Earned Aug 13, 2025 EDT
[DEPRECATED] Model evaluation on Vertex AI
Earned Aug 12, 2025 EDT
Deploy a RAG application with vector search in Firestore
Earned Aug 12, 2025 EDT
Edit images with Imagen
Earned Aug 12, 2025 EDT
Deploy and Evaluate Model Garden Models
Earned Aug 9, 2025 EDT
Extend Gemini with controlled generation and Tool use
Earned Aug 8, 2025 EDT
機器學習運作 (MLOps) 與 Vertex AI：模型評估
Earned Aug 2, 2025 EDT
Engineer Effective Prompts for Generative Models
Earned Jul 31, 2025 EDT
Explore Google's Gen AI Models
Earned Jul 30, 2025 EDT
[DEPRECATED] Evaluate ADK Agents with Vertex AI Gen AI Evaluation Service
Earned Jul 29, 2025 EDT
Generate and Edit Media in Agent Platform
Earned Jul 29, 2025 EDT
[DEPRECATED] Improve Performance by Fine-Tuning Foundation Models
Earned Jul 28, 2025 EDT
Find, Explore and Deploy Model Garden Models
Earned Jul 28, 2025 EDT
Empower Gen AI Apps with Tool Use
Earned Jul 23, 2025 EDT
Vector Search 和嵌入
Earned Jul 14, 2025 EDT
運用 BigQuery 建立嵌入項目、向量搜尋和 RAG
Earned Jul 14, 2025 EDT
Build search and recommendations applications with AI Applications
Earned Jul 14, 2025 EDT
運用 Gemini Enterprise Agent Platform 部署多代理系統
Earned Jul 8, 2025 EDT
Modernization Quiz: Modernize my existing applications
Earned Apr 11, 2025 EDT
Google Workspace with Gemini：AI 工作流程的基礎
Earned Jun 5, 2024 EDT
使用 Gemini 和 Streamlit 開發生成式 AI 應用程式
Earned Jun 4, 2024 EDT
[DEPRECATED] 使用 Gemini：數據資料學家和分析師
Earned May 22, 2024 EDT
[DEPRECATED] 使用 Gemini：資安工程師
Earned May 21, 2024 EDT
[DEPRECATED] 使用 Gemini：端對端 SDLC
Earned May 20, 2024 EDT
[DEPRECATED] 使用 Gemini：DevOps 工程師
Earned Apr 29, 2024 EDT
[DEPRECATED] 使用 Gemini：網路工程師
Earned Apr 18, 2024 EDT
[DEPRECATED] 使用 Gemini：雲端架構師
Earned Apr 16, 2024 EDT
[DEPRECATED] 使用 Gemini：應用程式開發人員
Earned Apr 16, 2024 EDT
Generative AI Fundamentals - 繁體中文
Earned Jul 5, 2023 EDT
負責任的 AI 技術簡介
Earned Jul 5, 2023 EDT
注意力機制
Earned May 27, 2023 EDT
大型語言模型簡介
Earned May 27, 2023 EDT
生成式 AI 簡介
Earned May 27, 2023 EDT
Creating Infrastructure on Google Cloud with Terraform
Earned Oct 5, 2022 EDT
Measure Site Reliability using Cloud Operations Suite
Earned Aug 11, 2022 EDT
雲端架構：設計、實作與管理
Earned Aug 9, 2022 EDT
Cloud Architecture - Design, Implement, and Manage
Earned Aug 9, 2022 EDT
DEPRECATED Google Cloud's Operations Suite
Earned Aug 8, 2022 EDT
Managing Cloud Infrastructure with Terraform
Earned May 16, 2022 EDT
Intermediate ML: TensorFlow on Google Cloud
Earned Mar 15, 2021 EDT
[Deprecated] Advanced ML: ML Infrastructure
Earned Mar 15, 2021 EDT
建立 Google Cloud 網路
Earned Jan 12, 2021 EST
在 Compute Engine 導入 Cloud Load Balancing
Earned Jul 7, 2020 EDT
在 Google Cloud 設定應用程式開發環境
Earned Jul 6, 2020 EDT
Google Kubernetes Engine Best Practices
Earned Feb 8, 2020 EST
DevOps 必備知識
Earned Feb 6, 2020 EST
雲端工程
Earned Nov 9, 2019 EST
Google Kubernetes Engine Best Practices: Security
Earned Aug 21, 2019 EDT
DEPRECATED Network Performance and Optimization
Earned Aug 1, 2019 EDT
Cloud Development
Earned Jul 8, 2019 EDT
Automate Deployment and Manage Traffic on a Google Cloud Network
Earned Jul 3, 2019 EDT
Cloud Adoption Strategies
Earned Jul 1, 2019 EDT
Google Cloud Solutions I: Scaling Your Infrastructure
Earned Jun 25, 2019 EDT
基本概念：基礎架構
Earned Jun 24, 2019 EDT
[DEPRECATED] Deploying Applications
Earned May 27, 2019 EDT
NCAA® March Madness®: Bracketology with Google Cloud
Earned Apr 2, 2019 EDT
基本概念：資料、機器學習和 AI
Earned Mar 19, 2019 EDT
[DEPRECATED] Data Engineering
Earned Mar 13, 2019 EDT
DEPRECATED Cloud Architecture
Earned Jan 27, 2019 EST
安全性與身分識別基礎知識
Earned Jan 25, 2019 EST
DEPRECATED Application Development - Python
Earned Jan 3, 2019 EST
Cloud SQL
Earned Jan 2, 2019 EST
Deprecated Kubernetes Solutions
Earned Oct 1, 2018 EDT
Google Cloud 中的 Kubernetes
Earned Sep 10, 2018 EDT
Google Cloud 必備知識
Earned May 11, 2018 EDT
完成「為 Google Cloud 設定服務帳戶和 IAM 角色」課程，即可獲得入門級技能徽章。這門課程會介紹服務帳戶、自訂角色，以及如何使用 gcloud 設定權限。
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完成 在 Google Cloud 部署 Kubernetes 應用程式 技能徽章中階課程，即可證明您具備下列技能： 設定及建構 Docker 容器映像檔、建立及管理 Google Kubernetes Engine (GKE) 叢集、運用 kubectl 有效 管理叢集，以及運用強大的持續推送軟體更新做法來部署 Kubernetes 應用程式。
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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。
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完成 在 Google Cloud 實作 Cloud 安全防護措施：基礎知識 技能徽章中階課程， 即可證明您具備下列技能：運用 Identity and Access Management (IAM) 建立及指派角色、 建立及管理服務帳戶、啟用虛擬私有雲 (VPC) 網路中的私人連線、 運用 Identity-Aware Proxy 限制應用程式存取權、 運用 Cloud Key Management Service (KMS) 管理金鑰和已加密資料，以及建立私人 Kubernetes 叢集。
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生成式 AI：瞭解基礎概念是 Gen AI Leader 學習路徑的第二門課程。在本課程中，您將瞭解 AI、機器學習和生成式 AI 的差異，以及各種類型的資料如何協助生成式 AI 解決業務難題，進而掌握生成式 AI 的基礎概念。您還能深入瞭解 Google Cloud 因應基礎模型限制的策略，以及開發、部署安全且負責任的 AI 技術時面臨的主要挑戰。
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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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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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Data stores represent a simple way to make content available to many types of generative AI applications, including search applications, recommendations engines, Gemini platform apps, Agent Development Kit (ADK) in Gemini platform 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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Complete the Evaluate Gen AI model and agent performance skill badge to demonstrate your ability to use the Gen AI evaluation service. You will evaluate models to select the best model for a given task, compare models against each other and evaluate the performance of agents.
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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Learn how to create Hybrid Search applications using Vector Search on Gemini platform to combine semantic searching with keyword search to return results based on both semantic meaning and keyword matching.
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Learn how to build your own Retrieval-Augmented Generation (RAG) solutions for greater control and flexibility than out-of-the-box implementations. Create a custom RAG solution using Agent Platform APIs, vector stores, and the LangChain framework.
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This course delves into the complexities of assessing the quality of large language model outputs. It examines the challenges enterprises face due to the subjective and sometimes incorrect nature of LLM responses, including hallucinations and inconsistent results. The course introduces various evaluation metrics for different tasks like classification, text generation, and question answering, such as Accuracy, Precision, Recall, F1 score, ROUGE, BLEU, and Exact Match. It also explores evaluation methods offered by Vertex AI LLM Evaluation Services, including computation-based, autorater, and human evaluation, providing insights into their application and benefits. Finally, the module covers how to unit test LLM applications within Vertex AI.
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This lab tests your ability to develop a real-world Generative AI Q&A solution using a RAG framework. You will use Firestore as a vector database and deploy a Flask app as a user interface to query a food safety knowledge base.
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Complete the Edit images with Imagen skill badge to demonstrate your skills with Imagen's mask modes and editing modes to edit images according to certain prompts.
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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In this skill bagde, you will demonstrate your ability to use and compare models available in the Vertex AI Model Garden. You'll deploy a model to a Vertex AI Endpoint, query other models via their API, and use Vertex AI's Gen AI evaluation service to measure the performance of multiple models.
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Complete the Extend Gemini with controlled generation and Tool use skill badge to demonstrate your proficiency in connecting models to external tools and APIs. This allows models to augment their knowledge, extend their capabilities and interact with external systems to take actions such as sending an email.
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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本課程針對評估生成式和預測式 AI 模型，向機器學習從業人員介紹相關的基礎工具、技術和最佳做法。模型評估是機器學習的重要領域，確保這類系統能在正式環境中提供可靠、準確且成效優異的結果。
學員將深入瞭解多種評估指標與方法，以及適用於不同模型類型和工作的應用方式。此外，也會特別介紹生成式 AI 模型帶來的獨特難題，並提供有效的應對策略。透過 Google Cloud Vertex AI 平台，學員將瞭解在模型挑選、最佳化和持續監控方面，該如何導入穩健的評估程序。
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Learn a variety of strategies and techniques to engineer effective prompts for generative models
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Learn how to leverage Gemini multimodal capabilities to process and generate text, images, and audio and to integrate Gemini through APIs to perform tasks such as content creation and summarization.
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Evaluation is important at every step of your Gen AI development process. In this course you will learn how to evaluate gen AI agents built using agent frameworks.
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This course dives into the world of media creation with Gemini Enterprise Agent Platform using Nano Banana and Veo. Learn to design text and image-based prompts to produce high-quality, consistent images, and captivating, cinematic video clips. You'll also learn to refine generated assets using core editing functions. Finally, this course guides you through multi-tool workflow implementations for creative control and consistency, empowering you to transform images into video clips and leverage Gemini for prompt writing assistance and feedback.
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Model tuning is an effective way to customize large models to your tasks. It's a key step to improve the model's quality and efficiency. Model tuning provides benefits such as higher quality results for your specific tasks and increased model robustness. You learn some of the tuning options available in Vertex AI and when to use them.
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Model Garden on Gemini platform is a model library that helps you discover, test, and deploy models from Google and Google partners. Learn how to explore the available models and select the right ones for your use case. And how to deploy and interact with Model Garden models through the Google Cloud console and APIs.
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An LLM-based application can process language in a way that resembles thought. But if you want to extend its capabilities to take actions by running other functions you have coded, you will need to use function calling. This can also be referred to as tool use. Additionally, you can give a model the ability to search Google or search a data store of documents to ground its responses. In other words, to base its answers on that information. In this course, you’ll explore these concepts.
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這堂課程會介紹 AI 搜尋技術、工具和應用程式。主題涵蓋使用向量嵌入執行語意搜尋；結合語意和關鍵字做法的混合型搜尋機制；以及運用檢索增強生成 (RAG) 技術建構有基準的 AI 代理，盡可能減少 AI 幻覺。您可以實際使用 Vertex AI Vector Search，打造智慧型搜尋引擎。
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這堂課程會說明 BigQuery 中的檢索增強生成 (RAG) 解決方案，協助您減少 AI 幻覺。當中介紹的 RAG 工作流程包含建立嵌入項目、搜尋向量空間，以及生成更符合需求的答案。另外，這堂課程會解釋這些步驟背後的概念與原因，以及實際運用 BigQuery 實作的方法。完成課程之後，學員將學會使用 BigQuery，以及 Gemini 和嵌入模型等生成式 AI 模型，建立 RAG pipeline 來處理自己的 AI 幻覺應用實例。
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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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本課程說明如何使用 Google Agent Development Kit 建構複雜的多代理系統。您將建構配備工具的虛擬服務專員，並透過從屬關係和流程定義互動方式。您將在本機執行代理，並部署至 Gemini Enterprise Agent Platform 的 Agent Runtime，將其做為代管代理流程執行；Agent Runtime 則處理基礎架構決策和資源調度作業。請注意，這些實驗室是根據這項產品的預先發布版製成。我們會進行維護更新，因此這些研究室將可能出現延遲。
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Transition your legacy applications to modern architectures using Google Cloud's containerization, serverless, and managed Kubernetes solution.
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客戶能透過 Gemini for Google Workspace 在 Google Workspace 使用生成式 AI 功能。本學習路徑會介紹 Gemini 的主要功能，並說明如何在 Google Workspace 善用這些功能，提高生產力和效率。
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完成 使用 Gemini 和 Streamlit 開發生成式 AI 應用程式 技能徽章中階課程，即可證明您具備下列技能： 生成文字、透過 Python SDK 和 Gemini API 呼叫函式，以及運用 Cloud Run 部署 Streamlit 應用程式。 您將瞭解如何以不同方式透過提示請 Gemini 生成文字、使用 Cloud Shell 測試及疊代 Streamlit 應用程式，隨後封裝成 Docker 容器並在 Cloud Run 中部署。
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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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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 保護雲端環境和資源。您將學到如何將工作負載範例部署到 Google Cloud 中的環境，以及運用 Gemini 找出並修復安全性設定錯誤。在實作研究室中，您也會體驗到 Gemini 如何改良雲端安全防護機制。
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，可協助工程師透過 Google Cloud 管理基礎架構。您將學到如何透過提示讓 Gemini 尋找和瞭解應用程式記錄檔、建立 GKE 叢集，以及研究如何打造建構環境。在實作研究室中，您也會瞭解 Gemini 如何改良開發運作的工作流程。
Duet AI 已更名為 Gemini，是我們新一代的模型。
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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，可協助網路工程師建立、更新及維護虛擬私有雲網路。您將瞭解如何透過提示讓 Gemini 為網路工作提供指引，獲得比搜尋結果更具體的資訊。在實作研究室中，您也會體驗到 Gemini 如何簡化 Google Cloud 虛擬私有雲網路的作業。
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，可協助管理員在 Google Cloud 佈建基礎架構。您將瞭解如何透過提示讓 Gemini 解釋基礎架構、部署 GKE 叢集，以及更新既有的基礎架構。在實作研究室中，您也會體驗到 Gemini 如何改良 GKE 的部署工作流程。
Duet AI 已更名為 Gemini，這是我們的新一代模型。
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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。
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完成「Introduction to Generative AI」、「Introduction to Large Language Models」和「Introduction to Responsible AI」課程，即可獲得技能徽章。通過最終測驗，就能展現您對生成式 AI 基本概念的掌握程度。
「技能徽章」是 Google Cloud 核發的數位徽章，用於表彰您對 Google Cloud 產品和服務的相關知識。您可以將技能徽章公布在社群媒體的個人資料中，向其他人分享您的成果。
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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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本課程將介紹注意力機制，說明這項強大技術如何讓類神經網路專注於輸入序列的特定部分。此外，也將解釋注意力的運作方式，以及如何使用注意力來提高各種機器學習任務的成效，包括機器翻譯、文字摘要和回答問題。
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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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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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In this quest you will get hands-on experience writing infrastructure as code with Terraform.
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Earn a skill badge by completing the Measure Site Reliability using Cloud Operations Suite quest, where you will learn how to set Service Level Indicators (SLIs), Service Level Objectives (SLOs), and Service Level Agreements (SLAs); create logs-based metrics to capture to capture specific issues and address them; define alerts to notify Site Reliability Engineers of issues in production environment, and troubleshoot application issues with Cloud Trace, Debugger, Profiler, Monitoring and Logging. 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 this skill badge quest, and the final assessment challenge lab, to receive a skill badge that you can share with your network.
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完成 雲端架構：設計、實作與管理 課程即可獲得 技能徽章，證明您具備下列技能： 使用 Apache 網路伺服器部署可公開存取的網站、使用開機指令碼設定 Compute Engine VM、 使用 Windows 防禦主機和防火牆規則設定安全的 RDP、建構 Docker 映像檔並部署至 Kubernetes 叢集，然後進行更新，以及建立 Cloud SQL 執行個體並匯入 MySQL 資料庫。 這個技能徽章課程是絕佳的 資源，可讓您瞭解Google Cloud 認證專業雲端架構師認證測驗涵蓋的主題。
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This quest of "Challenge Labs" gives the student preparing for the Google Cloud Certified Professional Cloud Architect certification hands-on practice with common business/technology solutions using Google Cloud architectures. Challenge Labs do not provide the "cookbook" steps, but require solutions to be built with minimal guidance, across many Google Cloud technologies. All labs have activity tracking, and in order to earn this badge you must score 100% in each lab. This quest is not easy and will put your Google Cloud technology skills to the test! Be aware that while practice with these labs will increase your knowledge and abilities, additional study, experience, and background in cloud architecture is recommended to prepare for this certification. Complete this quest to receive an exclusive Google Cloud digital badge.
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Learn the ins and outs of Google Cloud's operations suite, an important service for generating insights into the health of your applications. It provides a wealth of information in application monitoring, report logging, and diagnoses. These labs will give you hands-on practice with and will teach you how to monitor virtual machines, generate logs and alerts, and create custom metrics for application data. It is recommended that the students have at least earned a Badge by completing the Google Cloud Essentials. Looking for a hands on challenge lab to demonstrate your skills and validate your knowledge? On completing this course, enroll in and finish the challenge lab at the end of the Monitor and Log with Google Cloud Operations Suite to receive an exclusive Google Cloud digital badge.
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In this Quest, the experienced user of Google Cloud will learn how to describe and launch cloud resources with Terraform, an open source tool that codifies APIs into declarative configuration files that can be shared amongst team members, treated as code, edited, reviewed, and versioned. In these nine hands-on labs, you will work with example templates and understand how to launch a range of configurations, from simple servers, through full load-balanced applications.
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TensorFlow is an open source software library for high performance numerical computation that's great for writing models that can train and run on platforms ranging from your laptop to a fleet of servers in the Cloud to an edge device. This quest takes you beyond the basics of using predefined models and teaches you how to build, train and deploy your own on Google Cloud.
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Machine Learning is one of the most innovative fields in technology, and the Google Cloud Platform has been instrumental in furthering its development. With a host of APIs, Google Cloud has a tool for just about any machine learning job. In this advanced-level course, you will get hands-on practice with machine learning at scale and how to employ the advanced ML infrastructure available on Google Cloud.
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完成 建立 Google Cloud 網路 課程即可獲得技能徽章。這個課程將說明 部署及監控應用程式的多種方法，包括查看 IAM 角色及新增/移除 專案存取權、建立虛擬私有雲網路、部署及監控 Compute Engine VM、編寫 SQL 查詢、在 Compute Engine 部署及監控 VM，以及 使用 Kubernetes 透過多種方法部署應用程式。
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完成「在 Compute Engine 導入 Cloud Load Balancing」技能徽章入門課程，即可證明您具備下列技能： 在 Compute Engine 建立及部署虛擬機器， 以及設定網路和應用程式負載平衡器。
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只要修完「在 Google Cloud 設定應用程式開發環境」課程，就能獲得技能徽章。 在本課程中，您將學會如何使用以下技術的基本功能，建構和連結以儲存空間為中心的雲端基礎架構：Cloud Storage、Identity and Access Management、Cloud Functions 和 Pub/Sub。
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Get Anthos Ready. Demand for Google Kubernetes Engine is growing, and customers are looking to Google and its partners to provide in-depth technical knowledge. This first Google Kubernetes Engine-centric Quest of best practices hands-on labs will get you started containerizing to modernize in place , and then managing your deployed apps and services -- with monitoring, tracing, and logging.
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透過 DevOps 取得 競爭優勢。DevOps 作業涵蓋組織與文化層面，目標為加速推送軟體、 提高服務穩定性，並為所有軟體相關人員 建立共同擁有權。這堂課程說明如何使用 Google Cloud 提高軟體推送功能的速度、穩定性、可用性和安全性。 開發運作研究與評估計畫已新增至 Google Cloud。想知道自己的團隊表現如何嗎？完成 這個五道選擇題的測驗就能知道答案！
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本入門課程有別於其他課程。 透過這些實驗室，IT 專業人員將有機會實際練習， 熟悉出現在 Google Cloud 助理雲端工程師認證中的主題和服務。本課程包含多個專門的實驗室，從 IAM、網路建立 到 Kubernetes Engine 部署作業， 可全面驗收您的 Google Cloud 知識。請注意，雖然進行這些 實驗室可提升您的技能和能力，但仍建議同時詳閱 測驗指南和其他可用的準備資源。
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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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If you want to take your Google Cloud networking skills to the next level, look no further. This course is composed of labs that cover real-life use cases and it will teach you best practices for overcoming common networking bottlenecks. From getting hands-on practice with testing and improving network performance, to integrating high-throughput VPNs and networking tiers, Network Performance and Optimization is an essential course for Google Cloud developers who are looking to double down on application speed and robustness.
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The hands-on labs in this Quest are structured to give experienced app developers hands-on practice with the state-of-the-art developing applications in Google Cloud. The topics align with the Google Cloud Certified Professional Cloud Developer Certification. These labs follow the sequence of activities needed to create and deploy an app in Google Cloud from beginning to end. Be aware that while practice with these labs will increase your skills and abilities, it is recommended that you also review the exam guide and other available preparation resources.
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Networking is a principle theme of cloud computing. It’s the underlying structure of Google Cloud, and it’s what connects all your resources and services to one another. This course will cover essential Google Cloud networking services and will give you hands-on practice with specialized tools for developing mature networks. From learning the ins-and-outs of VPCs, to creating enterprise-grade load balancers, Automate Deployment and Manage Traffic on a Google Cloud Network will give you the practical experience needed so you can start building robust networks right away.
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Welcome to Cloud Adoption Strategies Click "Join this Game".
To modify your player name or avatar, go to your My Account page at https://google.qwiklabs.com.
Points are earned by completing the steps in the lab.... and bonus points are earned for speed! Be sure to complete each lab by selecting the END option to get the maximum points.
Please respect the GCP resource quotas that have been allocated. Otherwise, you'll waste your Game time and gain fewer points.
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In this course you will learn how you to harness serious Google Cloud power and infrastructure. The hands-on labs will give you use cases and you will be tasked with implementing scaling practices utilized by Google’s very own Solutions Architecture team. From developing enterprise grade load balancing and autoscaling, to building continuous delivery pipelines, Google Cloud Solutions I: Scaling your Infrastructure will teach you best practices for taking your Google Cloud projects to the next level.
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如果您是剛起步的雲端開發人員， 想在 Google Cloud Essentials 外獲得更多實作經驗，歡迎參加本課程。您將透過實作實驗室， 深入瞭解 Cloud Storage 和其他重要應用程式服務，例如： Monitoring 和 Cloud Functions。您將習得 在任何 Google Cloud 專案都適用的寶貴技能。
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The Google Cloud Platform provides many different frameworks and options to fit your application’s needs. In this introductory-level quest, you will get plenty of hands-on practice deploying sample applications on Google App Engine. You will also dive into other web application frameworks like Firebase, Wordpress, and Node.js and see firsthand how they can be integrated with Google Cloud.
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In this series of labs you will learn how to use BigQuery to analyze NCAA basketball data with SQL. Build a Machine Learning Model to predict the outcomes of NCAA March Madness basketball tournament games.
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大數據、機器學習和人工智慧 (AI) 是時下熱門的 電腦相關話題，但這些領域相當專業，就算想要入門 也難以取得教材或資料。幸好，Google Cloud 提供了此領域的多種服務，而且容易使用。 參加這堂入門課程，您就能踏出第一步， 開始學習運用 BigQuery、Cloud Speech API 以及 Video Intelligence 等工具。
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This advanced-level quest is unique amongst the other catalog 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 Data Engineer Certification. From Big Query, to Dataprep, to Cloud Composer, this quest is composed of specific labs that will put your Google Cloud data engineering knowledge to the test. Be aware that while practice with these labs will increase your skills and abilities, you will need other preparation, too. The exam is quite challenging and external studying, experience, and/or background in cloud data engineering is recommended. Looking for a hands on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, enroll in and finish the additional challenge lab at the end of the Engineer Data in the Google Cloud to receive an exclusive Google Cloud digital badge.
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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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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。
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In this advanced-level quest, you will learn the ins and outs of developing GCP applications in Python. The first labs will walk you through the basics of environment setup and application data storage with Cloud Datastore. Once you have a handle on the fundamentals, you will get hands-on practice deploying Python applications on Kubernetes and App Engine (the latter is the same framework that powers Snapchat!) With specialized bonus labs that teach user authentication and backend service development, this quest will give you practical experience so you can start developing robust Python applications straight away.
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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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Containerized applications have changed the game and are here to stay. With Kubernetes, you can orchestrate containers with ease, and integration with the Google Cloud Platform is seamless. In this advanced-level quest, you will be exposed to a wide range of Kubernetes use cases and will get hands-on practice architecting solutions over the course of 8 labs. From building Slackbots with NodeJS, to deploying game servers on clusters, to running the Cloud Vision API, Kubernetes Solutions will show you first-hand how agile and powerful this container orchestration system is.
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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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在這堂入門課程，您將實際練習使用 Google Cloud 的基礎工具和服務。本課程包含可選擇觀賞的影片， 針對實驗室涵蓋的概念提供更多背景資訊，協助您複習。「Google Cloud 必備知識」 是適合 Google Cloud 學員的第一堂課， 即使您尚未學習或不熟悉雲端知識， 也能從這堂課獲得實務經驗，並應用於第一項 Google Cloud 專案。不管是撰寫 Cloud Shell 指令 和部署第一部虛擬機器，還是在 Kubernetes Engine 或透過負載平衡執行應用程式， 「Google Cloud 必備知識」都是認識平台基本功能的最佳入門資源。
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