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Aniket Kadam

成为会员时间:2023

青铜联赛

36770 积分
Google Cloud 的 AI 和機器學習服務簡介 Earned Jun 16, 2024 EDT
使用 Dataplex 建構資料網格 Earned Jun 15, 2024 EDT
使用 BigQuery ML 為預測模型進行資料工程 Earned Jun 14, 2024 EDT
透過 BigQuery 建構資料倉儲 Earned Jun 14, 2024 EDT
在 Google Cloud 為機器學習 API 準備資料 Earned Jun 14, 2024 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud Earned Jun 14, 2024 EDT
Build Data Lakes and Data Warehouses on Google Cloud Earned Jun 14, 2024 EDT
Serverless Data Processing with Dataflow: Foundations Earned Jun 13, 2024 EDT
Google Cloud Big Data and Machine Learning Fundamentals Earned Jun 6, 2024 EDT
Building Resilient Streaming Systems on Google Cloud Platform Earned May 23, 2024 EDT
Preparing for your Professional Data Engineer Journey Earned Apr 29, 2024 EDT
Migrating Hadoop Workloads Earned Mar 30, 2024 EDT
Getting Started With Application Development Earned Mar 30, 2024 EDT
Preparing for your Professional Cloud Architect Journey Earned Aug 24, 2023 EDT
Certification Learning Path: Professional Cloud DevOps Engineer Earned Aug 22, 2023 EDT
在 Google Cloud 實作 DevOps 工作流程 Earned Aug 21, 2023 EDT
可靠的 Google Cloud 基礎架構:設計與程序 Earned Aug 19, 2023 EDT
Developing a Google SRE Culture Earned Aug 15, 2023 EDT
透過 Google Cloud Observability 監控及記錄系統狀態 Earned Aug 15, 2023 EDT
DEPRECATED Cloud Operations and Service Mesh with Anthos Earned Jul 27, 2023 EDT
在 Google Cloud 使用 Terraform 建構基礎架構 Earned Jul 8, 2023 EDT
建立 Google Cloud 網路 Earned Jul 8, 2023 EDT
在 Google Cloud 設定應用程式開發環境 Earned Jul 5, 2023 EDT
在 Compute Engine 導入 Cloud Load Balancing Earned Jul 5, 2023 EDT
Logging and Monitoring in Google Cloud Earned Jul 4, 2023 EDT
開始使用 Google Kubernetes Engine Earned Jul 4, 2023 EDT
彈性的 Google Cloud 基礎架構:資源調度與自動化 Earned Jul 3, 2023 EDT
重要的 Google Cloud 基礎架構:核心服務 Earned Jul 3, 2023 EDT
重要的 Google Cloud 基礎架構:基本概念 Earned Jul 2, 2023 EDT
Preparing for Your Associate Cloud Engineer Journey Earned Jul 2, 2023 EDT
Getting Started with Terraform for Google Cloud Earned Jul 1, 2023 EDT
Google Cloud 基礎知識:核心基礎架構 Earned Jul 1, 2023 EDT

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「使用 Dataplex 建構資料網格」技能徽章入門課程,即可證明您具備下列技能:使用 Dataplex 建構資料網格, 以利在 Google Cloud 維護資料安全性,並協助治理和探索資料。您將練習並測試自己的技能,包括在 Dataplex 為資產加上標記、指派 IAM 角色,以及評估資料品質。

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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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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 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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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 is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next, we talk about the Apache Beam vision and the benefits of the Beam Portability framework. The Beam Portability framework achieves the vision that a developer can use their favorite programming language with their preferred execution backend. We then show you how Dataflow allows you to separate compute and storage while saving money, and how identity, access, and management tools interact with your Dataflow pipelines. Lastly, we look at how to implement the right security model for your use case on Dataflow.

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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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This 1-week, accelerated on-demand course builds upon Google Cloud Platform Big Data and Machine Learning Fundamentals. Through a combination of video lectures, demonstrations, and hands-on labs, you'll learn to build streaming data pipelines using Google cloud Pub/Sub and Dataflow to enable real-time decision making. You will also learn how to build dashboards to render tailored output for various stakeholder audiences.

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This course helps learners create a study plan for the PDE (Professional Data Engineer) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.

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This workload aims to upskill Google Cloud partners to perform specific tasks associated with priority workloads. Learners will perform the tasks of migrating workloads from Hadoop environments to corresponding Google Cloud services and hosted products. The following will addressed will be: The Hadoop ecosystem and products Hadoop architecture and post migration architectures to Google Cloud Assessment Data transfer options Workload migrations, namely: Spark to Dataproc Serverless, Apache Oozie to Composer (Airflow), and Hive to BigQuery Security and governance Logging and Monitoring

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In this course, application developers learn how to design and develop cloud-native applications that seamlessly integrate managed services from Google Cloud. Through a combination of presentations, demos, and hands-on labs, participants learn how to apply best practices for application development and use the appropriate Google Cloud storage services for object storage, relational data, caching, and analytics. Completing one version of each lab is required. Each lab is available in Node.js. In most cases, the same labs are also provided in Python or Java. You may complete each lab in whichever language you prefer. This is the first course of the Developing Applications with Google Cloud series. After completing this course, enroll in the Securing and Integrating Components of your Application course.

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This course helps learners create a study plan for the PCA (Professional Cloud Architect) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.

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Good news! There’s a new updated version of this learning path available for you!Open the new Professional Cloud DevOps Engineer Certification Learning Path to begin, once you’ve selected the new path all your current progress will be reflected in the new version.

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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:使用 Cloud Source Repositories 建立 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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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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In many IT organizations, incentives are not aligned between developers, who strive for agility, and operators, who focus on stability. Site reliability engineering, or SRE, is how Google aligns incentives between development and operations and does mission-critical production support. Adoption of SRE cultural and technical practices can help improve collaboration between the business and IT. This course introduces key practices of Google SRE and the important role IT and business leaders play in the success of SRE organizational adoption.

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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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Course two of the Architecting Hybrid Cloud with Anthos series prepares students to operate and observe Anthos environments. Through presentations and hands-on labs, participants explore adjusting existing clusters, setting up advanced traffic routing policies, securing communication across workloads, and observing clusters in Anthos. This course is a continuation of course one, Multi-Cluster, Multi-Cloud with Anthos, and assumes direct experience with the topics covered in that course.

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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 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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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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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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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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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。這堂課結合了視訊講座、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,包括安全地建立互連網路、負載平衡、自動調度資源、基礎架構自動化,以及代管服務。

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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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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,尤其側重於 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、虛擬機器和應用程式服務等基礎架構元件。您會瞭解如何透過控制台和 Cloud Shell 使用 Google Cloud。另外,您也能瞭解雲端架構師的職責、基礎架構設計方法,以及具備虛擬私有雲 (VPC)、專案、網路、子網路、IP 位址、路徑和防火牆規則的虛擬網路設定。

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This course helps you structure your preparation for the Associate Cloud Engineer exam. You will learn about the Google Cloud domains covered by the exam and how to create a study plan to improve your domain knowledge.

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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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