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Apply your skills in Google Cloud console

Ooi Han Yi

成为会员时间：2020

白银联赛

20200 积分
DEPRECATED Google Cloud Solutions II: Data and Machine Learning Earned Aug 6, 2023 EDT
建立 Google Cloud 網路 Earned Aug 6, 2023 EDT
雲端工程 Earned Aug 6, 2023 EDT
Automate Deployment and Manage Traffic on a Google Cloud Network Earned Aug 6, 2023 EDT
設定 Google Cloud 網路 Earned Aug 6, 2023 EDT
Data Catalog Fundamentals Earned Aug 5, 2023 EDT
探索生成式 AI - Vertex AI Earned Aug 5, 2023 EDT
Intermediate ML: TensorFlow on Google Cloud Earned Aug 5, 2023 EDT
在 Google Cloud 部署 Kubernetes 應用程式 Earned Aug 5, 2023 EDT
在 Google Cloud 使用 Terraform 建構基礎架構 Earned Aug 5, 2023 EDT
透過 Google Cloud 建構網站 Earned Aug 5, 2023 EDT
使用 Firebase 開發無伺服器應用程式 Earned Aug 5, 2023 EDT
在 Google Cloud 設定應用程式開發環境 Earned Aug 4, 2023 EDT
在 Compute Engine 導入 Cloud Load Balancing Earned Aug 4, 2023 EDT
NCAA® March Madness®: Bracketology with Google Cloud Earned Nov 1, 2021 EDT
為 Looker 資訊主頁和報表準備資料 Earned Nov 1, 2021 EDT
在 Google Cloud 使用機器學習 API Earned Nov 1, 2021 EDT
DEPRECATED Explore Machine Learning Models with Explainable AI Earned Nov 1, 2021 EDT
DEPRECATED BigQuery for Marketing Analysts Earned Nov 1, 2021 EDT
Machine Learning APIs Earned Nov 1, 2021 EDT
運用 BigQuery ML 建立機器學習模型 Earned Nov 1, 2021 EDT
使用 BigQuery 進行機器學習 Earned Oct 31, 2021 EDT
DEPRECATED BigQuery Basics for Data Analysts Earned Oct 31, 2021 EDT
DEPRECATED BigQuery for Data Warehousing Earned Oct 31, 2021 EDT
Intro to ML: Image Processing Earned Oct 30, 2021 EDT
機器學習簡介：語言處理 Earned Oct 29, 2021 EDT
基本概念：資料、機器學習和 AI Earned Oct 29, 2021 EDT
在 Google Cloud 為機器學習 API 準備資料 Earned Oct 29, 2021 EDT
從 BigQuery 資料取得深入分析結果 Earned Oct 28, 2021 EDT

In this advanced-level quest, you will learn how to harness serious Google Cloud computing power to run big data and machine learning jobs. The hands-on labs will give you use cases, and you will be tasked with implementing big data and machine learning practices utilized by Google’s very own Solutions Architecture team. From running Big Query analytics on tens of thousands of basketball games, to training TensorFlow image classifiers, you will quickly see why Google Cloud is the go-to platform for running big data and machine learning jobs.

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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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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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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「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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Data Catalog is deprecated and will be discontinued on January 30, 2026. You can still complete this course if you want to. For steps to transition your Data Catalog users, workloads, and content to Dataplex Catalog, see Transition from Data Catalog to Dataplex Catalog (https://cloud.google.com/dataplex/docs/transition-to-dataplex-catalog). Data Catalog is a fully managed and scalable metadata management service that empowers organizations to quickly discover, understand, and manage all of their data. In this quest you will start small by learning how to search and tag data assets and metadata with Data Catalog. After learning how to build your own tag templates that map to BigQuery table data, you will learn how to build MySQL, PostgreSQL, and SQLServer to Data Catalog Connectors.

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探索生成式 AI - Vertex AI 課程包含一系列實驗室，幫助您瞭解 如何在 Google Cloud 使用生成式 AI。透過實驗室，您將瞭解 如何使用 Vertex AI PaLM API 系列模型，包括 text-bison、chat-bison、 和 textembedding-gecko。您也會瞭解提示設計、最佳做法、 以及這些模型如何用於構思、文字分類、文字擷取、文字 摘要等。您也會瞭解如何透過 Vertex AI 自訂訓練功能調整基礎模型， 並將模型部署至 Vertex AI 端點。

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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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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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完成透過 Google Cloud 建構網站技能徽章課程，即可獲得入門級技能徽章。 本課程以 Get Cooking in Cloud 系列影片為基礎， 涵蓋以下主題：在 Cloud Run 部署網站在 Compute Engine 託管網頁應用程式在 Google Kubernetes Engine 建立、 部署及擴充網站使用 Cloud Build 將單體式應用程式遷移至微服務架構

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完成 使用 Firebase 開發無伺服器應用程式 技能徽章中階課程， 即可證明您具備下列技能：使用 Firebase 架構及建構無伺服器的網頁應用程式、 運用 Firestore 管理資料庫、使用 Cloud Build 自動部署內容， 以及將 Google 助理功能整合至應用程式。

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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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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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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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完成「為 Looker 資訊主頁和報表準備資料」技能徽章入門課程， 即可證明您具備下列技能：可篩選、排序和 pivot 資料、合併不同的 Looker 探索結果， 還能使用函式和運算子建構 Looker 資訊主頁和報表，取得資料分析結果和圖表。

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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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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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Want to turn your marketing data into insights and build dashboards? Bring all of your data into one place for large-scale analysis and model building. Get repeatable, scalable, and valuable insights into your data by learning how to query it and using BigQuery. 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.

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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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Want to scale your data analysis efforts without managing database hardware? Learn the best practices for querying and getting insights from your data warehouse with this interactive series of BigQuery labs. 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.

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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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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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大家都知道，機器學習是發展最快的科技領域之一， 而 Google Cloud Platform 在這方面功不可沒。 GCP 提供多種 API，凡是與機器學習相關的任務，幾乎都能處理。您將在本入門課程的 實驗室，實際演練機器學習技術 在語言處理方面的應用，學會如何從文中擷取實體資訊、 執行情緒和語法分析，並使用 Speech-to-Text API 轉錄語音。

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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 Dataproc 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 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 Looker Studio 中 透過連結 BigQuery 資料建立報表。

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