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Ram Tripathi

Member since 2021

透過直覺式程式開發和 MCP,建構智慧雲端應用程式 Earned Jun 23, 2026 EDT
在 Google Cloud 打造生成式 AI 應用程式 Earned Jun 14, 2026 EDT
在 Agent Platform 設計提示 Earned Jun 14, 2026 EDT
使用 Gemini 和 Streamlit 開發生成式 AI 應用程式 Earned Jun 14, 2026 EDT
機器學習運作 (MLOps) 與 Vertex AI:模型評估 Earned Jun 14, 2026 EDT
生成式 AI 適用的機器學習運作 (MLOps) Earned Jun 14, 2026 EDT
負責任的 AI 技術簡介 Earned May 13, 2026 EDT
大型語言模型簡介 Earned May 13, 2026 EDT
在 Google Cloud 環境導入 DevSecOps 做法 Earned Jun 14, 2025 EDT
Architecting with Google Kubernetes Engine: Production Earned Jun 13, 2025 EDT
Multi-Cluster, Multi-Cloud with Anthos Earned Jun 12, 2025 EDT
Google Kubernetes Engine Best Practices: Security Earned Jun 12, 2025 EDT
Networking in Google Cloud: Routing and Addressing Earned Jun 9, 2025 EDT
在 Google Cloud 實作 Sensitive Data Protection Earned Jun 9, 2025 EDT
Introduction to Cloud Identity Earned Mar 28, 2025 EDT
重要的 Google Cloud 基礎架構:核心服務 Earned Nov 20, 2024 EST
安全性與身分識別基礎知識 Earned Nov 18, 2024 EST
DEPRECATED Site Reliability Engineering: Measuring and Managing Reliability Earned Nov 17, 2024 EST
透過 Google Cloud Observability 監控及記錄系統狀態 Earned Nov 14, 2024 EST
[Deprecated] Cloud Logging Earned Nov 14, 2024 EST
Security Best Practices in Google Cloud Earned Nov 6, 2024 EST
運用 Google Cloud 探索資料轉換功能 Earned Oct 29, 2024 EDT
Manage Security in Google Cloud Earned Oct 22, 2024 EDT
Networking in Google Cloud: Fundamentals Earned Oct 22, 2024 EDT
開始使用 Google Kubernetes Engine Earned Oct 22, 2024 EDT
Getting Started with Terraform for Google Cloud Earned Oct 21, 2024 EDT
Google Cloud 基礎知識:核心基礎架構 Earned Oct 21, 2024 EDT
透過 Google Cloud 進行數位轉型 Earned Oct 21, 2024 EDT
在 Google Cloud 使用 TensorFlow 分類圖像 Earned May 17, 2024 EDT
Production Machine Learning Systems Earned Apr 29, 2024 EDT
探索生成式 AI - Agent Platform Earned Apr 29, 2024 EDT
使用 BigQuery 進行機器學習 Earned Apr 29, 2024 EDT
透過 Vertex AI 建構及部署機器學習解決方案 Earned Apr 25, 2024 EDT
Machine Learning Operations (MLOps): Getting Started Earned Apr 25, 2024 EDT
生成式 AI 簡介 Earned Apr 10, 2024 EDT
Google Cloud 的 AI 和機器學習服務簡介 Earned Apr 9, 2024 EDT
在 Google Cloud 為機器學習 API 準備資料 Earned Nov 28, 2023 EST
使用 BigQuery ML 為預測模型進行資料工程 Earned Nov 26, 2023 EST
Build Batch Data Pipelines on Google Cloud Earned Nov 24, 2023 EST
Build Streaming Data Pipelines on Google Cloud Earned Nov 24, 2023 EST
Build a Certification Study Guide: PDE Exam Prep Earned Nov 23, 2023 EST
Smart Analytics, Machine Learning, and AI on Google Cloud Earned Nov 19, 2023 EST
Build Data Lakes and Data Warehouses on Google Cloud Earned Nov 19, 2023 EST
[DEPRECATED] Google Cloud Big Data and Machine Learning Fundamentals Earned Nov 19, 2023 EST
運用 BigQuery ML 建立機器學習模型 Earned Nov 18, 2023 EST
透過 BigQuery 建構資料倉儲 Earned Nov 15, 2023 EST
從 BigQuery 資料取得深入分析結果 Earned Nov 14, 2023 EST
Google Cloud Solutions I: Scaling Your Infrastructure Earned Jun 9, 2023 EDT
設定 Google Cloud 網路 Earned Jun 9, 2023 EDT
Google Kubernetes Engine 成本效益最佳化 Earned Jun 8, 2023 EDT
Mitigating Security Vulnerabilities on Google Cloud Earned Jun 8, 2023 EDT
可靠的 Google Cloud 基礎架構:設計與程序 Earned May 30, 2023 EDT
Logging and Monitoring in Google Cloud Earned May 29, 2023 EDT
雲端架構:設計、實作與管理 Earned May 29, 2023 EDT
Hybrid Cloud Service Mesh with Anthos Earned May 26, 2023 EDT
DEPRECATED Cloud Architecture Earned May 26, 2023 EDT
在 Google Cloud 部署 Kubernetes 應用程式 Earned Dec 9, 2022 EST
建立 Google Cloud 網路 Earned Dec 7, 2022 EST
建構安全的 Google Cloud 網路 Earned Dec 7, 2022 EST
Using the Cloud SDK Command Line Earned Dec 7, 2022 EST
在 Google Cloud 實作 Cloud 安全防護措施:基礎知識 Earned Dec 6, 2022 EST
在 Google Cloud 使用 Terraform 建構基礎架構 Earned Dec 6, 2022 EST
Google Cloud 必備知識 Earned Sep 8, 2022 EDT
在 Compute Engine 導入 Cloud Load Balancing Earned Jun 24, 2022 EDT
在 Google Cloud 設定應用程式開發環境 Earned Jun 17, 2022 EDT
基本概念:基礎架構 Earned Jun 17, 2022 EDT

完成「透過直覺式程式開發和 MCP,建構智慧雲端應用程式」課程,即可獲得技能徽章。在本課程中,您將學會如何利用 Google AI 程式設計助理和 MCP 伺服器的強大功能。

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大型語言模型 (LLM) 誕生之後,生成式 AI 應用程式帶來的嶄新使用者體驗,可說是幾乎前所未有。身為應用程式開發人員,您要如何在 Google Cloud,運用生成式 AI 建立出色的互動式應用程式? 本課程將帶您瞭解生成式 AI 應用程式,以及如何使用提示設計和檢索增強生成 (RAG),透過 LLM 建構強大的應用程式。我們也會介紹可用於正式環境的生成式 AI 應用程式架構。您將建構採用 LLM 和 RAG 的對話應用程式。

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完成 在 Agent Platform 設計提示 技能徽章入門課程,即可證明您具備下列技能: 在 Agent Platform 設計提示、分析圖片,以及運用多模態模型生成內容。瞭解如何建立有效的提示、引導生成式 AI 輸出內容, 以及將 Gemini 模型用於實際的行銷情境。

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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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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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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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In this course, you'll learn about Kubernetes and Google Kubernetes Engine (GKE) security; logging and monitoring; and using Google Cloud managed storage and database services from within GKE. This is the second course of the Architecting with Google Kubernetes Engine series. After completing this course, enroll in the Reliable Google Cloud Infrastructure: Design and Process course or the Hybrid Cloud Infrastructure Foundations with Anthos course.

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Course one of the Architecting Hybrid Cloud with Anthos series introduces participants to manage multi-cloud and hybrid Kubernetes deployments using Anthos. Through presentations and hands-on labs, participants explore planning and creating Anthos environments and building manageable and reliable multi-cluster Kubernetes infrastructure environments centered around Anthos and containers. This course is a continuation of Architecting with GKE and assumes direct experience with the technologies covered in that course.

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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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Welcome to the second course in the networking and Google Cloud series routing and addressing. In this course, we'll cover the central routing and addressing concepts that are relevant to Google Cloud's networking capabilities. Module one will lay the foundation by exploring network routing and addressing in Google Cloud, covering key building blocks such as routing IPv4, bringing your own IP addresses and setting up cloud DNS. In Module two will shift our focus to private connection options, exploring use cases and methods for accessing Google and other services privately using internal IP addresses. By the end of this course, you'll have a solid grasp of how to effectively route and address your network traffic within Google Cloud.

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完成 在 Google Cloud 實作 Sensitive Data Protection 技能徽章入門課程,證明您具備下列技能:使用 Sensitive Data Protection 服務 (包括 Cloud Data Loss Prevention API) 來檢查、遮蓋及去識別化 Google Cloud 中的機密資料。

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Introduction to Cloud Identity serves as the starting place for any new Cloud Identity, Identity/Access Management/Mobile Device Management admins as they begin their journey of managing and establishing security and access management best practices for their organization. This 15-30 hour accelerated, one-week course will leave you feeling confident to utilize the basic functions of the Admin Console to manage users, control access to services, configure common security settings, and much more. Through a series of introductory lessons, step-by-step hands-on exercises, Google knowledge resources, and knowledge checks, learners can expect to leave this training with all of the skills they need to get started as new Cloud Identity Administrators.

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這堂隨選密集課程會向參加人員說明 Google Cloud 提供的全方位彈性基礎架構和平台服務,並將重點放在 Compute Engine。這堂課程結合了視訊講座、示範和實作研究室,可讓參加人員探索及部署解決方案元素,例如網路、系統和應用程式服務等基礎架構元件。另外,這堂課也會介紹如何部署實用的解決方案,包括客戶提供的加密金鑰、安全性和存取權管理機制、配額與帳單,以及資源監控功能。

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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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Service level indicators (SLIs) and service level objectives (SLOs) are fundamental tools for measuring and managing reliability. In this course, students learn approaches for devising appropriate SLIs and SLOs and managing reliability through the use of an error budget.

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完成 透過 Google Cloud Observability 監控及記錄系統狀態 技能徽章入門課程, 即可證明您具備下列技能:監控 Compute Engine 中的虛擬機器、 運用 Cloud Monitoring 監管多項專案、在 Cloud Functions 延伸應用監控和記錄功能、 建立和傳送自訂應用程式指標,以及根據自訂指標設定 Cloud Monitoring 快訊。

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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 self-paced training course gives participants broad study of security controls and techniques on Google Cloud. Through recorded lectures, demonstrations, and hands-on labs, participants explore and deploy the components of a secure Google Cloud solution, including Cloud Storage access control technologies, Security Keys, Customer-Supplied Encryption Keys, API access controls, scoping, shielded VMs, encryption, and signed URLs. It also covers securing Kubernetes environments.

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雲端技術是強大的資產,與資料搭配後,將成為創新和提升客戶體驗的催化劑。「運用 Google Cloud 探索資料轉換功能」課程將探討機構如何運用雲端,讓資料更易於存取、做為行動依據和更有價值。 這門課程是 Cloud Digital Leader 學習路徑的一部分,旨在協助個人在職務上成長,並打造企業的未來。

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This course provides foundational knowledge for securing your Google Cloud environment. You will learn Google Cloud's security model, including Zero Trust principles and the Shared Fate model. You will configure identity services like Cloud Identity and Workforce Identity Federation, manage access control using Identity and Access Management (IAM) roles, conditions, and policies, and implement network security controls such as VPCs, Cloud Firewall tiers, and VPC Service Controls to build a secure perimeter. As the foundation of the Security in Google Cloud learning path, this course establishes the core principles required for two subsequent specialized tracks: the five-course Security Best Practices in Google Cloud series and the four-course Mitigate Threats in Google Cloud series.

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Networking in Google cloud is a 6 part course series. Welcome to the first course of our six part course series, Networking in Google Cloud: Fundamentals.  This course provides a comprehensive overview of core networking concepts, including networking fundamentals, virtual private clouds (VPCs), and the sharing of VPC networks. Additionally, the course covers network logging and monitoring techniques. 

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歡迎參加「開始使用 Google Kubernetes Engine」課程。Kubernetes 是位於應用程式和硬體基礎架構之間的軟體層。如果您對這項技術感興趣,這堂課程可以滿足您的需求。有了 Google Kubernetes Engine,您就能在 Google Cloud 中以代管服務的形式使用 Kubernetes。 本課程的目標在於介紹 Google Kubernetes Engine (常簡稱為 GKE) 的基本概念,以及如何將應用程式容器化,以便在 Google Cloud 中執行。課程首先會初步介紹 Google Cloud,隨後簡介容器、Kubernetes、Kubernetes 架構和 Kubernetes 作業。

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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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「Google Cloud 基礎知識:核心基礎架構」介紹了在使用 Google Cloud 時會遇到的重要概念和術語。本課程會透過影片和實作實驗室,介紹並比較 Google Cloud 的多種運算和儲存服務,同時提供重要的資源和政策管理工具。

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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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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 course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training, and how to write distributed training models with custom estimators. This is the second course of the Advanced Machine Learning on Google Cloud series. After completing this course, enroll in the Image Understanding with TensorFlow on Google Cloud course.

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「探索生成式 AI - Agent Platform」課程包含一系列實驗室, 介紹如何在 Google Cloud 使用生成式 AI。完成這些實驗室之後,您會瞭解如何 在 Agent Platform 使用 Gemini 模型、提示詞設計技巧與最佳做法,以及 AI 在構思想法和文字分類、擷取、摘要等方面的用途。您也會瞭解如何運用 Agent Platform 的自訂訓練功能調整基礎模型, 並部署至 Agent Platform 中的端點。

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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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完成 透過 Vertex AI 建構及部署機器學習解決方案 課程,即可瞭解如何使用 Google Cloud 的 Vertex AI 平台、AutoML 和自訂訓練服務, 訓練、評估、調整、解釋及部署機器學習模型。 這個技能徽章課程適合專業數據資料學家和機器學習 工程師,完成即可取得中階技能徽章。技能 徽章是 Google Cloud 核發的獨家數位徽章, 用於肯定您在 Google Cloud 產品和服務方面的精通程度, 代表您已通過測驗,能在互動式實作環境應用相關知識。完成這個技能徽章課程 和結業評量挑戰實驗室,就能獲得數位徽章, 並與親友分享。

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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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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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本課程介紹 Google Cloud 的 AI 和機器學習 (ML) 功能,著重說明如何開發生成式和預測式 AI 專案。我們也會探討「從資料到 AI」整個生命週期都適用的技術、產品和工具,並透過互動式練習,協助資料科學家、AI 開發人員和機器學習工程師精進專業知識。

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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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完成使用 BigQuery ML 為預測模型進行資料工程技能徽章中階課程, 即可證明自己具備下列知識與技能:運用 Dataprep by Trifacta 建構連至 BigQuery 的資料轉換 pipeline; 使用 Cloud Storage、Dataflow 和 BigQuery 建構「擷取、轉換及載入」(ETL) 工作負載, 以及使用 BigQuery ML 建構機器學習模型。

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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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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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Learn how to use Gemini Notebook to create a personalized study guide for the Professional Data Engineer certification exam. You'll review Gemini Notebook features, create a notebook in Gemini Notebook, and learn how to use a study guide to practice for a certification exam.

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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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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.

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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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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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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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完成 從 BigQuery 資料取得深入分析結果 技能徽章入門課程,即可證明您具備下列技能: 撰寫 SQL 查詢、查詢公開資料表、將樣本資料載入 BigQuery、使用 BigQuery 的查詢驗證工具 排解常見語法錯誤,以及在 Data Studio 中 透過連結 BigQuery 資料建立報表。

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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「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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完成 Google Kubernetes Engine 成本效益最佳化 技能徽章中階課程, 即可證明您具備下列技能:建立及管理多租戶叢集、依據命名空間監控資源使用量、 設定自動調度叢集和 Pod 資源以提升效能、設定負載平衡以最佳化 資源分配,以及導入有效性和完備性探測,確保應用程式維持健康並符合成本效益。

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In this self-paced training course, participants learn mitigations for attacks at many points in a Google Cloud-based infrastructure, including Distributed Denial-of-Service attacks, phishing attacks, and threats involving content classification and use. They also learn about the Security Command Center, cloud logging and audit logging, and using Forseti to view overall compliance with your organization's security policies.

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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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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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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 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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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 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「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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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.

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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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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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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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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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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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