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在 Google Cloud 控制台中运用您的技能

Vinitha Raj

成为会员时间：2020

白银联赛

11200 积分
在 Vertex AI 上构建和部署机器学习解决方案 Earned Jul 31, 2022 EDT
在 Google Cloud 中使用 Kubernetes Earned Jul 30, 2022 EDT
Learn to Earn Cloud Challenge: Machine Learning Engineering Skills Earned Jun 30, 2022 EDT
Learn to Earn Cloud Challenge: Sports Data Analysis Skills Earned Jun 30, 2022 EDT
Learn to Earn Cloud Data Challenge: Data Analyst Skills Earned Jun 29, 2022 EDT
Managing Security in Google Cloud Earned Jun 19, 2022 EDT
Mitigating Security Vulnerabilities on Google Cloud Earned Jun 17, 2022 EDT
Modernize Infrastructure and Applications with Google Cloud Earned Jun 17, 2022 EDT
Deprecated : Managing Machine Learning Projects with Google Cloud Earned Jun 17, 2022 EDT
Scaling with Google Cloud Operations Earned Jun 17, 2022 EDT
Applying Machine Learning to your Data with Google Cloud Earned Jun 17, 2022 EDT
[DEPRECATED] Network Security by Palo Alto Networks Earned Jun 16, 2022 EDT
Google Cloud Solutions I: Scaling Your Infrastructure Earned Jun 16, 2022 EDT
安全与身份基础知识 Earned Jun 15, 2022 EDT
Google Cloud 云计算基础课程：Google Cloud 中的基础设施 Earned Jun 15, 2022 EDT
云架构：设计、实施和管理 Earned Jun 15, 2022 EDT
Google Cloud 云计算基础课程：Google Cloud 中的网络服务和安全性 Earned Jun 14, 2022 EDT
Google Cloud Big Data and Machine Learning Fundamentals Earned Jun 14, 2022 EDT
Google Cloud 云计算基础课程：Google Cloud 中的数据、机器学习和 AI Earned Jun 14, 2022 EDT
Automate Interactions with Contact Center AI Earned Jun 13, 2022 EDT
DEPRECATED Explore Machine Learning Models with Explainable AI Earned Jun 12, 2022 EDT
利用 BigQuery ML 构建预测模型时的数据工程处理 Earned Jun 12, 2022 EDT
在 Cloud Run 上开发无服务器应用 Earned Jun 10, 2022 EDT
使用 BigQuery 构建数据仓库 Earned Jun 9, 2022 EDT
通过 BigQuery ML 创建机器学习模型 Earned Jun 8, 2022 EDT
构建安全的 Google Cloud 网络 Earned Jun 7, 2022 EDT
在 Google Cloud　上使用 Machine Learning API Earned Jun 1, 2022 EDT
Data Science on Google Cloud: Machine Learning Earned Nov 14, 2020 EST
Getting Started with Go on Google Cloud Earned Nov 14, 2020 EST
Google Cloud Run Serverless Workshop Earned Nov 13, 2020 EST
DEPRECATED Google Cloud Solutions II: Data and Machine Learning Earned Nov 13, 2020 EST
在 Cloud Data Fusion 上构建无代码流水线 Earned Nov 13, 2020 EST
在 Google Cloud 上设置应用开发环境 Earned Nov 10, 2020 EST
DEPRECATED BigQuery for Data Warehousing Earned Nov 10, 2020 EST
Data Catalog Fundamentals Earned Nov 10, 2020 EST
机器学习简介：语言处理 Earned Nov 9, 2020 EST
Intro to ML: Image Processing Earned Nov 9, 2020 EST

完成在 Vertex AI 上构建和部署机器学习解决方案课程，赢取中级技能徽章。 在此课程中，您将了解如何使用 Google Cloud 的 Vertex AI Platform、AutoML 以及自定义训练服务来 训练、评估、调优、解释和部署机器学习模型。 此技能徽章课程的目标受众是专业的数据科学家和机器学习 工程师。 技能徽章是由 Google Cloud 颁发的专属数字徽章，旨在认可 您对 Google Cloud 产品与服务的熟练度；您需要在 交互式实操环境中参加考核，证明自己运用所学知识的能力后才能获得此徽章。完成此技能徽章课程 和作为最终评估的实验室挑战赛，即可获得数字徽章， 在您的人际圈中炫出自己的技能。

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Kubernetes 是最受欢迎的容器编排系统， Google Kubernetes Engine 专为支持 Google Cloud 中的托管式 Kubernetes 部署 而设计。在本高级课程中，您将亲自动手配置 Docker 映像、容器，并部署功能完备的 Kubernetes Engine 应用。 此课程将帮助您掌握在工作流中集成容器编排所需的 实用技能。 想要参加实操实验室挑战赛， 展示您的技能并检验所学知识？完成本课程后，不妨继续参与这项额外的 实验室挑战赛，赢得 Google Cloud 专属数字徽章。 该挑战赛位于在 Google Cloud 上部署 Kubernetes 应用课程的结尾处。

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These labs help you get started with the skills you need to develop and train ML models. At the end of each lab, you’ll have hands-on experience with one or more of Google Cloud’s powerful data tools. Complete this game to earn a badge, and you’ll be one step closer to completing the challenge. Race the clock to increase your score and watch your name rise on the leaderboard!

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These labs help you get started with the skills you need to analyze sports-related data. At the end of each lab, you’ll have hands-on experience with one or more of Google Cloud’s powerful data tools. Complete this game to earn a badge, and you’ll be one step closer to completing the challenge. Race the clock to increase your score and watch your name rise on the leaderboard!

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Welcome to the Learn to Earn Cloud Data Challenge! These labs help you get started with data analysis skills. At the end of each lab, you’ll have hands-on experience with one or more of Google Cloud’s powerful data tools. Complete this game to earn a badge, and you’ll be one step closer to completing the challenge. Race the clock to increase your score and watch your name rise on the leaderboard!

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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 Identity, Resource Manager, IAM, Virtual Private Cloud firewalls, Cloud Load Balancing, Cloud Peering, Cloud Interconnect, and VPC Service Controls. This is the first course of the Security in Google Cloud series. After completing this course, enroll in the Security Best Practices in Google Cloud course.

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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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Many traditional enterprises use legacy systems and applications that can't stay up-to-date with modern customer expectations. Business leaders often have to choose between maintaining their aging IT systems or investing in new products and services. "Modernize Infrastructure and Applications with Google Cloud" explores these challenges and offers solutions to overcome them by using cloud technology. Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.

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Business professionals in non-technical roles have a unique opportunity to lead or influence machine learning projects. If you have questions about machine learning and want to understand how to use it, without the technical jargon, this course is for you. Learn how to translate business problems into machine learning use cases and vet them for feasibility and impact. Find out how you can discover unexpected use cases, recognize the phases of an ML project and considerations within each, and gain confidence to propose a custom ML use case to your team or leadership or translate the requirements to a technical team.

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Organizations of all sizes are embracing the power and flexibility of the cloud to transform how they operate. However, managing and scaling cloud resources effectively can be a complex task. Scaling with Google Cloud Operations explores the fundamental concepts of modern operations, reliability, and resilience in the cloud, and how Google Cloud can help support these efforts. Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.

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In this course, we define what machine learning is and how it can benefit your business. You'll see a few demos of ML in action and learn key ML terms like instances, features, and labels. In the interactive labs, you will practice invoking the pretrained ML APIs available as well as build your own Machine Learning models using just SQL with BigQuery ML.

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The move to Google® Cloud public cloud services is underway, and requirements for cloud security are real. Palo Alto Networks® offers innovative and comprehensive protection as you adopt public cloud services. The Palo Alto Networks solutions complement the native Google Cloud security toolset, including network, host and cloud-native security delivered through APIs for comprehensive protection. In this Quest, you will get hands-on practice with the Palo Alto Networks virtual next-generation firewall running in Google Cloud. You will gain practical experience that you can apply to improve visibility and security for your applications and workloads.

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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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安全是 Google Cloud 服务绝不妥协的核心原则，为此， Google Cloud 开发了特定工具，为您的所有项目提供 安全与身份保障。本入门课程中，您将了解 用于管理用户和虚拟机账号的 Google Cloud Identity and Access Management (IAM) 服务 并进行实操练习。您还将通过 配置 VPC 和 VPN 获取网络安全方面的实践经验，并了解有哪些工具可用于 抵御安全威胁和防止数据泄露。

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Google Cloud 云计算基础课程面向云计算零基础或经验较少的人群。本课程概述了云计算基础知识、大数据和机器学习的核心概念，以及 Google Cloud 在其中的定位与应用方式。 完成本系列课程后，学员将能够清晰阐述这些概念，并掌握部分实操技能。 课程应按以下顺序完成： 1. Google Cloud 云计算基础课程：云计算基础知识 2. Google Cloud 云计算基础课程：Google Cloud 中的基础设施 3. Google Cloud 云计算基础课程：Google Cloud 中的网络服务和安全性 4. Google Cloud 云计算基础课程：Google Cloud 中的数据、机器学习和 AI

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完成 云架构：设计、实施和管理课程，赢取技能徽章，展示您在以下方面的技能：使用 Apache Web 服务器部署可公开访问的网站；使用启动脚本配置 Compute Engine 虚拟机； 使用 Windows 堡垒主机和防火墙规则配置安全 RDP；构建 Docker 映像并将其部署到 Kubernetes 集群，然后进行更新；以及创建 CloudSQL 实例并导入 MySQL 数据库。 此技能徽章课程是非常有用的资源， 可帮助您理解 Google Cloud 认证 Professional Cloud Architect 认证考试中将会出现的主题。

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Google Cloud 云计算基础课程面向几乎没有云计算背景或经验的人士。本课程概述了云计算基础知识、大数据和机器学习的核心概念，以及 Google Cloud 在其中的定位与应用方式。 完成本课程系列后，学员将能够阐述这些概念，并展示一定的实操技能。 课程应按以下顺序完成： 1. Google Cloud 云计算基础课程：云计算基础知识 2. Google Cloud 云计算基础课程：Google Cloud 中的基础设施 3. Google Cloud 云计算基础课程：Google Cloud 中的网络服务和安全性 4. Google Cloud 云计算基础课程：Google Cloud 中的数据、机器学习和 AI 本课是第三门课程，介绍云端自动化和管理工具以及如何构建安全网络。

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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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Google Cloud 云计算基础课程面向没有或很少有云计算基础或经验的人群。本课程概述了云计算基础知识、大数据和机器学习的核心概念，以及 Google Cloud 在其中的定位与应用方式。 完成本系列课程后，学员将能够清晰阐述这些概念，并掌握一些实际操作技能。 课程应按以下顺序完成： 1. Google Cloud 云计算基础课程：云计算基础知识 2. Google Cloud 云计算基础课程：Google Cloud 中的基础设施 3. Google Cloud 云计算基础课程：Google Cloud 中的网络服务和安全性 4. Google Cloud 云计算基础课程：Google Cloud 中的数据、机器学习和 AI 本课程是该系列课程的最后一门，回顾了托管式大数据服务、机器学习及其价值，以及如何通过获得技能徽章来进一步展示您在 Google Cloud 方面的技能。

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Earn a skill badge by completing the Automate Interactions with Contact Center AI quest, where you will learn about the features of Contact Center AI, including how to Build a virtual agent, Design conversation flows for your virtual agent; Add a phone gateway to your virtual agent; Use Dialogflow for troubleshooting; Review logs and debug your virtual agent. 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 the skill badge quest, and final assessment challenge lab, to receive a digital badge that you can share with your network.

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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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完成中级技能徽章课程利用 BigQuery ML 构建预测模型时的数据工程处理， 展示自己在以下方面的技能：利用 Dataprep by Trifacta 构建 BigQuery 数据转换流水线； 利用 Cloud Storage、Dataflow 和 BigQuery 构建提取、转换和加载 (ETL) 工作流； 以及利用 BigQuery ML 构建机器学习模型。

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完成在 Cloud Run 上开发无服务器应用技能徽章中级课程， 展示您在以下方面的技能：集成 Cloud Run 与 Cloud Storage 以管理数据， 使用 Cloud Run 和 Pub/Sub 设计弹性异步系统架构， 构建依托 Cloud Run 技术的 REST API 网关，以及在 Cloud Run 上构建和部署服务。

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完成中级技能徽章课程使用 BigQuery 构建数据仓库，展示以下技能： 联接数据以创建新表、排查联接故障、使用并集附加数据、创建日期分区表， 以及在 BigQuery 中使用 JSON、数组和结构体。

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完成中级技能徽章课程通过 BigQuery ML 创建机器学习模型，展示您在以下方面的技能： 使用 BigQuery ML 创建和评估机器学习模型，以执行数据预测。

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完成构建安全的 Google Cloud 网络课程，赢取技能徽章。在此课程中，您将了解与网络有关的众多 资源，以便在 Google Cloud 上构建、扩缩和保护自己的应用。

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完成在 Google Cloud　上使用 Machine Learning API 课程，赢取高级技能徽章。 在本课程中，您将了解以下机器学习和 AI 技术的基本功能： Cloud Vision API、Cloud Translation API 和 Cloud Natural Language API。

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This is the second of two Quests of hands-on labs derived from the exercises from the book Data Science on Google Cloud Platform, 2nd Edition by Valliappa Lakshmanan, published by O'Reilly Media, Inc. In this second Quest, covering chapter 9 through the end of the book, you extend the skills practiced in the first Quest, and run full-fledged machine learning jobs with state-of-the-art tools and real-world data sets, all using Google Cloud tools and services.

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Get started with Go (Golang) by reviewing Go code, and then creating and deploying simple Go apps on Google Cloud. Go is an open source programming language that makes it easy to build fast, reliable, and efficient software at scale. Go runs native on Google Cloud, and is fully supported on Google Kubernetes Engine, Compute Engine, App Engine, Cloud Run, and Cloud Functions. Go is a compiled language and is faster and more efficient than interpreted languages. As a result, Go requires no installed runtime like Node, Python, or JDK to execute.

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Twelve years ago Lily started the Pet Theory chain of veterinary clinics, and has been expanding rapidly. Now, Pet Theory is experiencing some growing pains: their appointment scheduling system is not able to handle the increased load, customers aren't receiving lab results reliably through email and text, and veteranerians are spending more time with insurance companies than with their patients. Lily wants to build a cloud-based system that scales better than the legacy solution and doesn't require lots of ongoing maintenance. The team has decided to go with serverless technology. For the labs in the Google Cloud Run Serverless Quest, you will read through a fictitious business scenario in each lab and assist the characters in implementing a serverless solution. 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…

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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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本课程提供 Cloud Data Fusion 的实操练习。Cloud Data Fusion 是云原生的 无代码数据集成平台。受益于此，ETL 开发者、数据工程师和分析师 可以利用预构建的转换工具和连接器，轻松构建和 部署流水线，而无需操心编码问题。本课程首先提供了 一个快速入门实验，让学习者熟悉 Cloud Data Fusion 界面。接下来， 学员将尝试运行批处理和实时流水线，并使用内置的 Wrangler 插件对数据执行一些有趣的转换。

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完成“在 Google Cloud 上设置应用开发环境”课程，赢取技能徽章；通过该课程，您将了解如何使用以下技术的基本功能来构建和连接以存储为中心的云基础设施： Cloud Storage、Identity and Access Management、Cloud Functions 和 Pub/Sub。

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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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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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众所周知，机器学习是发展最快的技术领域之一， Google Cloud Platform 在推动其发展方面发挥了重要作用。 GCP 提供了一系列 API，几乎可以满足任何机器学习作业的需求。在 本入门课程中，您将了解机器学习在语言处理方面的运用， 通过实操实验学习 如何从文本中提取实体，执行情感和语法分析，以及 使用 Speech-to-Text API 进行转写。

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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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