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S Venkit

Member since 2020

Diamond League

35320 points
使用 ADK 和 MCP 建構協作式多代理系統 Earned Nov 9, 2026 EST
建立您的第一個 Gemini Enterprise 應用程式 Earned Oct 30, 2026 EDT
企業 AI 代理與用途 Earned Oct 30, 2026 EDT
運用工具讓 AI 代理具備所要能力 Earned Oct 29, 2026 EDT
管理 AI 代理的記憶與狀態 Earned Oct 27, 2026 EDT
使用 Agent Development Kit (ADK) 建造 AI 代理：工程設計 Earned Aug 18, 2026 EDT
AI 代理行為最佳化調整 Earned Jan 26, 2026 EST
使用 Agent Development Kit (ADK) 打造您的第一個 AI 代理 Earned Jan 25, 2026 EST
AI 代理基礎知識 Earned Jan 23, 2026 EST
Gemini Enterprise 簡介 Earned Dec 11, 2025 EST
[DEPRECATED] 使用 Gemini：端對端 SDLC Earned Nov 7, 2025 EST
運用 Gemini Code Assist 簡化應用程式開發作業 Earned Nov 2, 2025 EST
設定組織專用的 Gemini Code Assist Earned Nov 2, 2025 EDT
在 Google Cloud 使用 TensorFlow 分類圖像 Earned Jul 19, 2025 EDT
[DEPRECATED] AI Boost Bites: Customer insights with NotebookLM Earned Jun 15, 2025 EDT
開發人員的負責任 AI 技術：公平性與偏誤 Earned Apr 2, 2025 EDT
使用 Gemini 多模態功能和多模態 RAG 檢查複合型文件 Earned Aug 2, 2024 EDT
Vector Search 和嵌入 Earned Jun 1, 2024 EDT
Vertex AI Studio 簡介 Earned May 30, 2024 EDT
建立圖像說明生成模型 Earned May 29, 2024 EDT
Transformer 和 BERT 模型 Earned May 29, 2024 EDT
編碼器-解碼器架構 Earned May 28, 2024 EDT
注意力機制 Earned May 28, 2024 EDT
圖像生成簡介 Earned May 28, 2024 EDT
Level 3: GenAIus Conversations Earned Feb 8, 2024 EST
Cloud Hero DevOps Skills Earned Apr 2, 2023 EDT
Cloud Hero BigQuery Skills Earned Apr 1, 2023 EDT
Cloud Hero Infra Skills Earned Mar 30, 2023 EDT
Qwiklabs Trivia Game Earned Apr 5, 2022 EDT
DEPRECATED Explore Machine Learning Models with Explainable AI Earned Dec 29, 2021 EST
Cloud SQL Earned Dec 29, 2021 EST
Intermediate ML: TensorFlow on Google Cloud Earned Dec 28, 2021 EST
透過 Google Cloud 進行數位轉型 Earned Oct 17, 2021 EDT
Learn to Earn Cloud Challenge: Architecture Earned Oct 11, 2021 EDT
Learn to Earn Cloud Challenge: Essentials Earned Oct 2, 2021 EDT
透過 BigQuery 建構資料倉儲 Earned Jul 23, 2021 EDT
Save the Day! Earned Jul 23, 2021 EDT
Intro to ML: Image Processing Earned Jul 22, 2021 EDT
在 Google Cloud 使用機器學習 API Earned Jul 21, 2021 EDT
Machine Learning APIs Earned Jul 20, 2021 EDT
運用 BigQuery ML 建立機器學習模型 Earned Jul 18, 2021 EDT
使用 BigQuery 進行機器學習 Earned Jul 15, 2021 EDT
基本概念：資料、機器學習和 AI Earned Jul 10, 2021 EDT
在 Google Cloud 為機器學習 API 準備資料 Earned Jul 10, 2021 EDT
Google Cloud Solutions I: Scaling Your Infrastructure Earned May 16, 2021 EDT
在 Google Cloud 使用 Terraform 建構基礎架構 Earned Apr 9, 2021 EDT
Managing Cloud Infrastructure with Terraform Earned Mar 10, 2021 EST
Google Kubernetes Engine Best Practices: Security Earned Mar 8, 2021 EST
Deprecated Kubernetes Solutions Earned Mar 8, 2021 EST
VM Migration Earned Mar 2, 2021 EST
[DEPRECATED] Secure Workloads in Google Kubernetes Engine Earned Feb 13, 2021 EST
在 Google Cloud 實作 DevOps 工作流程 Earned Jan 21, 2021 EST
DevOps 必備知識 Earned Jan 9, 2021 EST
DEPRECATED BigQuery for Data Warehousing Earned Jan 8, 2021 EST
Google Cloud 中的 Kubernetes Earned Oct 26, 2020 EDT
在 Google Cloud 部署 Kubernetes 應用程式 Earned Oct 17, 2020 EDT
建構安全的 Google Cloud 網路 Earned Oct 1, 2020 EDT
雲端工程 Earned Oct 1, 2020 EDT
雲端架構：設計、實作與管理 Earned Sep 29, 2020 EDT
建立 Google Cloud 網路 Earned Sep 19, 2020 EDT
DEPRECATED Cloud Architecture Earned Sep 10, 2020 EDT
設定 Google Cloud 網路 Earned Sep 9, 2020 EDT
在 Google Cloud 設定應用程式開發環境 Earned Aug 24, 2020 EDT
基本概念：基礎架構 Earned Aug 22, 2020 EDT
Automate Deployment and Manage Traffic on a Google Cloud Network Earned Aug 20, 2020 EDT
在 Compute Engine 導入 Cloud Load Balancing Earned Aug 9, 2020 EDT

瞭解如何使用 Google 的 Agent Development Kit (ADK) 和 Model Context Protocol (MCP)，設計、協調及部署多代理系統。本課程將以旅遊規劃服務為例，說明如何透過自訂防護機制，調度管理專用代理網路。完成本課程後，您將瞭解如何運用設計模式和部署策略，打造穩固且可擴充的 AI 工作流程。

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建立您的第一個 Gemini Enterprise 應用程式，獲得技能徽章！將各種資料來源連接到您的應用程式，建立強大的統合式搜尋和分析引擎。掌握 Deep Research 代理、多代理構思和 NotebookLM 等進階功能，進行重點分析。

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瞭解 AI 代理如何發揮更高的業務影響力，包括根據您的 KPI 規劃要使用的代理類型，以及探索能解決實際瓶頸的用途。您也將認識各種無程式碼到高程式碼解決方案，瞭解 Gemini Enterprise 如何協助建構和自動調度合適的代理。

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您已建立具有進階設定功能的代理，可開始賦予代理實際應用能力。為代理配備工具，使代理能搜尋網路、執行程式碼、查詢資料庫和執行自訂動作。將代理從聰明的回覆者，轉變為能採取行動的得力助手。 查看 Gemini Enterprise Agent Ready (GEAR) 計畫的其他內容。 加入社群論壇，提出問題並參與討論。

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您已建構能回覆查詢的基本 LLM 代理，是時候讓代理有狀態。利用工作階段狀態建構能維持對話脈絡、記住使用者偏好，並提供個人化體驗的代理。將代理從無狀態的回覆者轉為智慧助理。 查看 Gemini Enterprise Agent Ready (GEAR) 計畫的其他內容。 加入社群論壇，提出問題並參與討論

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完成「使用 Agent Development Kit (ADK) 建造 AI 代理：工程設計」技能徽章中階課程，即可證明您具備下列技能： 提出實際的語言模型研究問題、建構簡單的權杖化工具、準備資料集來訓練 Transformer 語言模型，以及執行小型語言模型的訓練迴圈。

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您已經成功打造第一個代理，是時候迎接更進階的挑戰。在本課程中，您將學習如何應用進階指令、選擇模型、建立規劃能力及結構化輸出模式，藉此將基本 AI 代理轉為高明又精確的助理，讓您的技能更上一層樓。查看 Gemini Enterprise Agent Ready (GEAR) 計畫的其他內容。 加入社群論壇，提出問題並參與討論。

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使用 Agent Development Kit (ADK) 建構、設定及執行您的第一個 AI 代理，將自身代理知識化為實際成果。 在這堂實作課程，您會設立完整的 ADK 開發環境，並使用 Python 程式碼和 YAML 設定打造代理，然後透過多個介面執行。您也會瞭解定義代理行為的核心參數，將課程 1 的學習成果轉化為實際運作的程式碼。 查看 Gemini Enterprise Agent Ready (GEAR) 計畫的其他內容。

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本課程介紹 AI 代理的基礎知識，並探討代理的實際應用價值。代理可為開發人員、架構師和技術決策者奠定基礎，協助他們從目標導向的自主行為角度來理解 AI 系統。

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本課程介紹 Gemini Enterprise，這個強大的平台結合 AI 代理、企業搜尋功能、Gemini Notebook 和智慧資料存取權，可協助組織解決營運難題。學員將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、描述其架構，以及此平台如何管理不同職務的資料存取與隱私權事宜。

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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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本課程適合各程度的開發人員，主要介紹 Gemini Code Assist 的核心功能與特色。這項 Google Cloud AI 輔助協作工具是應用程式開發的好幫手，提供程式碼智慧建議、自動補全、即時偵錯和重構輔助等實用功能。您將瞭解 Gemini Code Assist 如何大幅提升工作效率和程式碼品質，並助您省下寶貴時間，專心處理較具效益且更有趣的工作。

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本課程專為 Google Cloud 開發人員和 DevOps 工程師設計，建議的修習對象為熟悉 Google Cloud 控制台基本操作，且需要為組織設定 Gemini Code Assist 者。課程將介紹 Gemini Code Assist 的優點、比較不同版本的功能，並展示如何在組織內設定及管理 Gemini Code Assist。

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完成「在 Google Cloud 使用 TensorFlow 分類圖像」技能徽章中階課程， 瞭解如何使用 TensorFlow 和 Vertex AI 建立及訓練機器學習模型， 即可獲得技能徽章。在 Vertex AI Workbench 中，你主要會和使用者自行管理的筆記本 互動。

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This video covers how NotebookLM can revolutionize customer insight gathering from call or chat transcripts. You'll learn to upload PDF transcripts of hundreds of conversations (even multilingual ones!) and quickly extract key themes, trending topics, and actionable insights without listening for hours. Discover how to save findings, share notebooks, and even generate interactive podcast summaries of your data.

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本課程旨在說明負責任 AI 技術的概念和 AI 開發原則，同時介紹各項技術，在實務上找出公平性和偏誤，減少 AI/機器學習做法上的偏誤。我們也將探討實用方法和工具，透過 Google Cloud 產品和開放原始碼工具，導入負責任 AI 技術的最佳做法。

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完成 使用 Gemini 多模態功能和多模態 RAG 檢查複合型文件 技能徽章中階課程，即可證明您具備下列技能： 透過 Gemini 多模態功能，使用多模態提示從文字和影像資料擷取資訊、生成影片說明，以及擷取影片以外的額外資訊； 透過 Gemini 的多模態檢索增強生成 (RAG) 功能，為含有文字和圖片的文件建構中繼資料、取得所有相關文字分塊，以及顯示引用資料。

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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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本課程會介紹 Vertex AI Studio。您可以運用這項工具和生成式 AI 模型互動、根據商業構想設計原型，並投入到正式環境。透過身歷其境的應用實例、有趣的課程及實作實驗室，您將能探索從提示到正式環境的生命週期，同時學習如何將 Vertex AI Studio 運用在多模態版 Gemini 應用程式、提示設計、提示工程和模型調整。這個課程的目標是讓您能運用 Vertex AI Studio，在專案中發揮生成式 AI 的潛能。

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本課程說明如何使用深度學習來建立圖像說明生成模型。您將學習圖像說明生成模型的各個不同組成部分，例如編碼器和解碼器，以及如何訓練和評估模型。在本課程結束時，您將能建立自己的圖像說明生成模型，並使用模型產生圖像說明文字。

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這堂課程將說明變換器架構，以及基於變換器的雙向編碼器表示技術 (BERT) 模型，同時帶您瞭解變換器架構的主要組成 (如自我注意力機制) 和如何用架構建立 BERT 模型。此外，也會介紹 BERT 適用的各種任務，像是文字分類、問題回答和自然語言推論。課程預計約 45 分鐘。

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本課程概要說明解碼器與編碼器的架構，這種強大且常見的機器學習架構適用於序列對序列的任務，例如機器翻譯、文字摘要和回答問題。您將認識編碼器與解碼器架構的主要元件，並瞭解如何訓練及提供這些模型。在對應的研究室逐步操作說明中，您將學習如何從頭開始使用 TensorFlow 寫程式，導入簡單的編碼器與解碼器架構來產生詩詞。

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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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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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Prompts are essential for guiding a Gen AI model to generate relevant and desired content. Start GenAIus conversations and earn a Google Cloud credential! No prior experience required!

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Welcome Gamers! Learn to navigate that iterative journey efficiently and effectively using Google Cloud's operations tools!, all while having fun! Deploy a Kubernetes cluster along with a service using Terraform. You will compete to see who can finish the game with the highest score. Earn the points by completing the steps in the lab.... and get bonus points for speed! Be sure to click "End" when you're done with each lab to get the maximum points. All players will be awarded the game badge.

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Get hands-on practice with Google Cloud! You will compete with your peers to see who can finish this game with the most points. Earn points by completing the labs accurately and receive bonus points for speed! Be sure to click “End” where you’re done with each lab to be rewarded your points.

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Get hands-on practice with Google Cloud! You will compete with your peers to see who can finish this game with the most points. Speed and accuracy will be used to calculate your scores — earn points by completing the labs accurately and bonus points for speed! Be sure to click “End” where you’re done with each lab to be rewarded your points.

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Ready for this week's Trivia? Complete all the labs and add this premium badge to your profile. Get credits worth $25 to continue your cloud learning journey. Hurry and join today as the game closes soon. Access code: 1q-trivia-422.

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Earn a skill badge by completing the Explore Machine Learning Models with Explainable AI quest, where you will learn how to do the following using Explainable AI: build and deploy a model to an AI platform for serving (prediction), use the What-If Tool with an image recognition model, identify bias in mortgage data using the What-If Tool, and compare models using the What-If Tool to identify potential bias. 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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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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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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數位轉型是現代組織必經的關鍵旅程，而建立穩固的雲端運算基礎，是跨出有意義創新的第一步。本課程「透過 Google Cloud 進行數位轉型」，將會介紹核心技術和策略框架，協助組織革新營運方式，並探討基本的雲端概念、全球網路基礎架構和共同責任模式，幫助主管在雲端之路上自信前行。本課程是 Cloud Digital Leader 學習路徑的一部分，旨在促進個人職能發展，並打造企業的未來。

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Welcome to the Learn To Earn Cloud Challenge architecture track! These eight labs give you a blueprint of GCP's building blocks. At the end of each lab, you'll have hands-on experience with another tool or service to add to your resume. Complete this game to earn the Architecture game badge, and you'll be one step closer to collecting all four Learn to Earn Cloud Challenge badges (see "what's next" below for more information). Race the clock to increase your score and watch your name rise on the leaderboard. Good luck!

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Welcome to the Learn To Earn Cloud Challenge! These eight labs give you a quick hands-on introduction to eight different GCP tools and services. At the end of each lab, you'll have another skill to add to your list. Complete this game to earn the Essentials game badge, and you'll be one step closer to collecting all four Learn to Earn Cloud Challenge badges (see "what's next" below for more information). Race the clock to increase your score and watch your name rise on the leaderboard. Good luck!

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完成 透過 BigQuery 建構資料倉儲 技能徽章中階課程，即可證明您具備下列技能： 彙整資料以建立新資料表、排解彙整作業問題、利用聯集附加資料、建立依日期分區的資料表， 以及在 BigQuery 使用 JSON、陣列和結構體。

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You work at a startup and you’re 6 weeks away from launching your new product. It’s a game changer and sure to land successfully, but your company needs to run lean between now and launch day. Everyone is pitching in to be smart and control costs.That’s where today’s game begins. See which team can save the most money through cost optimization. Each lab in this game walks you through a particular GCP tool that helps you monitor and optimize your spend. The points you score individually as you complete each lab will add up to your team’s total! Join the game, then join a team by updating your player name and avatar. At the end of the game, we’ll total all the points for both teams and declare a winning team. Every player on the winning team will receive 50 free Qwiklabs credits.

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Using large scale computing power to recognize patterns and "read" images is one of the foundational technologies in AI, from self-driving cars to facial recognition. The Google Cloud Platform provides world class speed and accuracy via systems that can utilized by simply calling APIs. With these and a host of other APIs, GCP has a tool for just about any machine learning job. In this introductory quest, you will get hands-on practice with machine learning as it applies to image processing by taking labs that will enable you to label images, detect faces and landmarks, as well as extract, analyze, and translate text from within images.

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完成「在 Google Cloud 使用機器學習 API」課程，即可獲得進階技能徽章。本課程說明以下機器學習和 AI 技術的基本功能： Cloud Vision API、Cloud Translation API 和 Cloud Natural Language API。

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It's no secret that machine learning is one of the fastest growing fields in tech, and Google Cloud 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 APIs by taking labs like Detect Labels, Faces, and Landmarks in Images with the Cloud Vision API. Looking for a hands-on challenge lab to demonstrate your skills and validate your knowledge? Enroll in and finish the additional challenge lab at the end of this quest to receive an exclusive Google Cloud digital badge.

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完成「運用 BigQuery ML 建立機器學習模型」技能徽章中階課程，即可證明您具備下列技能： 可使用 BigQuery ML 建立及評估機器學習模型，並根據資料進行預測。

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不想花費大把時間，想在幾分鐘內只靠 SQL，就建立好機器學習模型嗎？透過 BigQuery ML，資料分析師可以運用現有的 SQL 工具和技巧，建立、訓練、評估模型， 並使用模型進行預測，降低機器學習的使用門檻。在 本系列的實驗室，您會測試不同類型的模型，瞭解 優良模型應具備的條件。

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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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完成 在 Google Cloud 為機器學習 API 準備資料 技能徽章入門課程，即可證明您具備下列技能： 使用 Dataprep by Trifacta 清理資料、在 Dataflow 執行資料管道、在 Managed Service for Apache Spark 建立叢集和執行 Apache Spark 工作，以及呼叫機器學習 API，包含 Cloud Natural Language API、Google Cloud Speech-to-Text API 和 Video Intelligence API。

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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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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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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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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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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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Google Cloud’s four step structured Cloud Migration Path Methodology provides a defined and repeatable path for users to follow when migrating and modernizing Virtual Machines. In this quest, you will get hands-on practice with Google’s current solution set for VM assessment, planning, migration, and modernization. You will start by analyzing your lab environment and building assessment reports with CloudPhysics and StratoZone, then build a landing zone within Google Cloud leveraging Terraform’s infrastructure-as-code templates, next you will manually transform a two-tier application into a cloud-native workload running on Kubernetes, and finally, transform a VM workload into Kubernetes with Migrate for Anthos and migrate a VM between cloud environments.

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Earn a skill badge by completing the Secure Workloads in Google Kubernetes Engine quest, where you learn about security at scale on Google Kubernetes Engine (GKE) including how to: migrate containers from virtual machines to Google Kubernetes Engine, restrict network connections in GKE using firewalls and Network Policies, use role-based access controls (RBAC) in GKE, use Binary Authorization for security controls of your images, secure applications in GKE using 3 access levels: host, network, Kubernetes API, and harden GKE cluster configurations. 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 在 Google Cloud 實作 DevOps 工作流程 技能徽章中階課程， 即可證明您具備下列技能：建立與管理 Git 存放區、 在 Google Kubernetes Engine (GKE) 發布、管理和調度 Deployment， 以及建立 CI/CD 管道，自動建構容器映像檔與執行 GKE 部署作業。

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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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Looking to build or optimize your data warehouse? Learn best practices to Extract, Transform, and Load your data into Google Cloud with BigQuery. In this series of interactive labs you will create and optimize your own data warehouse using a variety of large-scale BigQuery public datasets. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights. 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 this quest to receive an exclusive Google Cloud digital badge.

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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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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「建構安全的 Google Cloud 網路」課程，即可獲得技能徽章。本課程將說明多項網路相關 資源，協助您在 Google Cloud 建構、調度資源和保護應用程式。

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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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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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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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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「設定 Google Cloud 網路」課程，即可獲得技能徽章。 您將瞭解如何在 Google Cloud Platform 執行基本的網路工作，包括建立自訂網路、新增子網路防火牆規則，還有建立 VM 並測試 VM 之間的通訊延遲。

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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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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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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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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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