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Aryan Pandya

成为会员时间:2023

青铜联赛

24830 积分
Machine Learning Operations (MLOps): Getting Started徽章 Machine Learning Operations (MLOps): Getting Started Earned Oct 28, 2024 EDT
Kubernetes in Google Cloud徽章 Kubernetes in Google Cloud Earned Oct 28, 2024 EDT
Manage Kubernetes in Google Cloud徽章 Manage Kubernetes in Google Cloud Earned Oct 28, 2024 EDT
App Building with AppSheet徽章 App Building with AppSheet Earned Jul 29, 2024 EDT
Getting Started with Google Kubernetes Engine - 繁體中文徽章 Getting Started with Google Kubernetes Engine - 繁體中文 Earned Jun 30, 2024 EDT
Essential Google Cloud Infrastructure: Core Services - 繁體中文徽章 Essential Google Cloud Infrastructure: Core Services - 繁體中文 Earned Jun 12, 2024 EDT
Essential Google Cloud Infrastructure: Foundation - 繁體中文徽章 Essential Google Cloud Infrastructure: Foundation - 繁體中文 Earned Jun 2, 2024 EDT
Google Cloud Fundamentals: Core Infrastructure - 繁體中文徽章 Google Cloud Fundamentals: Core Infrastructure - 繁體中文 Earned May 30, 2024 EDT
Develop Your Google Cloud Network徽章 Develop Your Google Cloud Network Earned May 17, 2024 EDT
Optimize Costs for Google Kubernetes Engine徽章 Optimize Costs for Google Kubernetes Engine Earned May 17, 2024 EDT
Build Infrastructure with Terraform on Google Cloud徽章 Build Infrastructure with Terraform on Google Cloud Earned May 2, 2024 EDT
在 Google Cloud 設定應用程式開發環境徽章 在 Google Cloud 設定應用程式開發環境 Earned Apr 15, 2024 EDT
Implement Load Balancing on Compute Engine徽章 Implement Load Balancing on Compute Engine Earned Apr 9, 2024 EDT
Intro to ML: Image Processing徽章 Intro to ML: Image Processing Earned Mar 10, 2024 EDT
Google Cloud Compute and Scalability for AWS Professionals徽章 Google Cloud Compute and Scalability for AWS Professionals Earned Oct 7, 2023 EDT
Preparing for your Professional Cloud Architect Journey徽章 Preparing for your Professional Cloud Architect Journey Earned May 9, 2023 EDT

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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Kubernetes is the most popular container orchestration system, and Google Kubernetes Engine was designed specifically to support managed Kubernetes deployments in Google Cloud. In this advanced-level quest, you will get hands-on practice configuring Docker images, containers, and deploying fully-fledged Kubernetes Engine applications. This quest will teach you the practical skills needed for integrating container orchestration into your own workflow. Looking for a hands-on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, finish the additional Challenge Lab at the end of the Deploy to Kubernetes in Google Cloud Quest to receive an exclusive Google Cloud digital badge.

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Earn a skill badge by completing the Manage Kubernetes in Google Cloud quest, where you will learn about Google Kubernetes Engine and multiple deployment approaches including how to: configure and build images to run and debug Docker containers, build Kubernetes Engine clusters, and manage them with kubectl, deploy Kubernetes applications using deployments and continuous delivery techniques. 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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Earn a skill badge by completing the App Building with AppSheet quest, where you learn how to build, configure, and publish apps using AppSheet. 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, and the final assessment challenge lab, to receive a digital badge that you can share with your network.

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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,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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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研究室,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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完成 Develop Your Google Cloud Network 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。 「技能徽章」是 Google Cloud 核發的獨家數位徽章,用於肯定 您對 Google Cloud 產品和服務的精通程度,代表您已通過測驗, 能在互動式實作環境中應用相關知識。完成這個技能徽章課程和 結業評量挑戰研究室,即可取得技能徽章並 與親友分享。

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Complete the intermediate Optimize Costs for Google Kubernetes Engine skill badge to demonstrate skills in the following: creating and managing multi-tenant clusters, monitoring resource usage by namespace, configuring cluster and pod autoscaling for efficiency, setting up load balancing for optimal resource distribution, and implementing liveness and readiness probes to ensure application health and cost-effectiveness. 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 course and the final assessment challenge lab to receive a skill badge that you can share with your network.

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Complete the intermediate Build Infrastructure with Terraform on Google Cloud skill badge to demonstrate skills in the following: Infrastructure as Code (IaC) principles using Terraform, provisioning and managing Google Cloud resources with Terraform configurations, effective state management (local and remote), and modularizing Terraform code for reusability and organization. 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 course and the final assessment challenge lab to receive a skill badge that you can share with your network.

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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。 「技能徽章」是 Google Cloud 核發的獨家數位徽章,用於表彰您相當熟悉 Google Cloud 產品與服務,並已通過測驗,能在互動式實作環境中應用相關知識。只要完成這個技能徽章課程和最終評量挑戰研究室,即可取得技能徽章並與親友分享成就。

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完成 Implement Load Balancing on Compute Engine 技能徽章入門課程,即可證明您具備下列技能: 編寫 gcloud 指令和使用 Cloud Shell、在 Compute Engine 建立及部署虛擬機器, 以及設定網路和 HTTP 負載平衡器。 「技能徽章」是 Google Cloud 核發的 獨家數位徽章,用於肯定您在 Google Cloud 產品與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中應用相關 知識。完成這個課程及挑戰研究室 最終評量,即可取得技能徽章並與親友分享。

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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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This is the second course of a four-course series for cloud architects and engineers with existing AWS knowledge. It aims to compare Google Cloud and AWS solutions and guide professionals on their use. This course focuses on compute resources and load balancing in Google Cloud. The learner will apply the knowledge of using virtual machines and load balancers in AWS to explore the similarities and differences with configuring and managing compute resources and load balancers in Google Cloud. Learners will get hands-on practice building and managing Google Cloud resources.

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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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