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INAAM ULLAH

成为会员时间：2020

钻石联赛

19934 积分
Model Armor：保障 AI 部署安全 Earned Apr 27, 2026 EDT
为组织配置 Gemini Code Assist Earned Apr 27, 2026 EDT
借助 Gemini Code Assist 简化应用开发 Earned Apr 27, 2026 EDT
Gemini Enterprise 简介 Earned Apr 27, 2026 EDT
使用有状态流创建对话智能体 Earned Apr 27, 2026 EDT
利用 Vertex AI 实现机器学习运维 (MLOps)：模型评估 Earned Apr 27, 2026 EDT
适用于生成式 AI 的机器学习运维 (MLOps) Earned Apr 27, 2026 EDT
使用 Agent Platform 探索生成式 AI Earned Apr 14, 2026 EDT
构建 Gemini Enterprise for Customer Experience 架构 Earned Apr 11, 2026 EDT
在 Agent Platform 中设计提示 Earned Apr 10, 2026 EDT
在 Google Cloud 上构建网站 Earned Apr 10, 2026 EDT
AI 智能体简介 Earned Apr 10, 2026 EDT
负责任的 AI 简介 Earned Apr 10, 2026 EDT
大型语言模型简介 Earned Apr 10, 2026 EDT
生成式 AI 简介 Earned Apr 10, 2026 EDT
[DEPRECATED] Secure Workloads in Google Kubernetes Engine Earned May 8, 2022 EDT
为 Looker 信息中心和报告准备数据 Earned May 7, 2022 EDT
[DEPRECATED] Build Interactive Apps with Google Assistant Earned May 7, 2022 EDT
利用 BigQuery ML 构建预测模型时的数据工程处理 Earned May 7, 2022 EDT
构建安全的 Google Cloud 网络 Earned May 7, 2022 EDT
设置 Google Cloud 网络 Earned May 7, 2022 EDT
在 Google Cloud 上实施云安全基础措施 Earned May 6, 2022 EDT
通过 BigQuery ML 创建机器学习模型 Earned May 6, 2022 EDT
使用 Google Cloud Observability 进行监控和记录 Earned May 6, 2022 EDT
在 Google Cloud 中实施 DevOps 工作流 Earned May 6, 2022 EDT
使用 BigQuery 构建数据仓库 Earned May 6, 2022 EDT
云架构：设计、实施和管理 Earned May 19, 2021 EDT
在 Google Cloud 上使用 Terraform 构建基础设施 Earned May 19, 2021 EDT
开发 Google Cloud 网络 Earned May 19, 2021 EDT
在 Google Cloud 上设置应用开发环境 Earned May 18, 2021 EDT
为 Compute Engine 实现云负载均衡 Earned May 18, 2021 EDT
[DEPRECATED] Data Engineering Earned Oct 2, 2020 EDT
Data Science on Google Cloud Earned Sep 30, 2020 EDT
[Deprecated] Advanced ML: ML Infrastructure Earned Sep 28, 2020 EDT
[Deprecated] Data Science on Google Cloud: Machine Learning Earned Sep 27, 2020 EDT
Automate Interactions with Contact Center AI Earned Sep 26, 2020 EDT
DEPRECATED Explore Machine Learning Models with Explainable AI Earned Sep 26, 2020 EDT
在 Google Cloud　上使用 Machine Learning API Earned Sep 26, 2020 EDT
Machine Learning APIs Earned Sep 26, 2020 EDT
在 Google Cloud 上为机器学习 API 准备数据 Earned Sep 26, 2020 EDT
Intermediate ML: TensorFlow on Google Cloud Earned Sep 25, 2020 EDT
NCAA® March Madness®: Bracketology with Google Cloud Earned Sep 23, 2020 EDT
DEPRECATED BigQuery Basics for Data Analysts Earned Sep 23, 2020 EDT
机器学习简介：语言处理 Earned Sep 23, 2020 EDT
Intro to ML: Image Processing Earned Sep 23, 2020 EDT
在 Google Cloud 中使用 Kubernetes Earned Jul 19, 2020 EDT
[Deprecated] Cloud Logging Earned Jul 14, 2020 EDT
Automate Deployment and Manage Traffic on a Google Cloud Network Earned Jul 3, 2020 EDT
基准：数据、机器学习和 AI Earned Jul 2, 2020 EDT
Baseline: Deploy & Develop Earned Jun 26, 2020 EDT
Cloud SQL Earned Jun 26, 2020 EDT
了解自己的 Google Cloud 成本 Earned Jun 25, 2020 EDT
安全与身份基础知识 Earned Jun 25, 2020 EDT
基准：基础架构 Earned Jun 24, 2020 EDT
Google Cloud 基础知识 Earned Jun 24, 2020 EDT

本课程回顾了 Model Armor 的基本安全功能，并让您能够使用该服务。您将了解与 LLM 相关的安全风险，以及 Model Armor 如何保护您的 AI 应用。

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本课程面向为组织配置 Gemini Code Assist 的 Google Cloud 开发者和 DevOps 工程师，需事先掌握 Google Cloud 控制台的基本知识。本课程介绍了 Gemini Code Assist 的优势，并比较了不同 Gemini Code Assist 版本的功能。本课程还介绍了如何在组织内配置和管理 Gemini Code Assist。

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本课程专为各个级别的开发者打造，您将学到 Gemini Code Assist 的核心特性和功能。Gemini Code Assist 依托 AI 技术，可协助您在 Google Cloud 上进行应用开发。从智能代码建议、自动补全、实时错误检测到重构辅助，您将发现 Gemini Code Assist 如何显著提升开发效率和代码质量，帮助您节省宝贵时间，专注于更具价值和趣味性的任务。

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本课程将介绍 Gemini Enterprise，它是一个集 AI 智能体、企业搜索、Gemini Notebook 和智能数据访问功能于一身的强大平台，旨在帮助组织应对各种挑战。学员通过学习真实案例并动手实操，将能够把 Gemini Enterprise 的各项功能与实际的业务需求联系起来，说明 Gemini Enterprise 的架构以及它如何处理不同角色的数据访问和隐私安全问题。

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探索 Conversational Agents 中的流，并了解如何使用语言模型构建具有确定性的聊天和语音体验。探索驱动因素、意图和实体等关键概念，以及如何使用这些概念来创建对话智能体。

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本课程能让机器学习从业者掌握评估生成式和预测式 AI 模型的基本工具、方法和最佳实践。要确保机器学习系统在实际运用中提供可靠、准确、高效的结果，做好模型评估至关重要。 学员将深入了解各项评估指标、方法及如何在不同模型类型和任务中适当应用这些指标和方法。课程将着重介绍生成式 AI 模型带来的独特挑战，并提供有效解决这些挑战的策略。通过利用 Google Cloud 的 Vertex AI Platform，学员可学习如何在模型选择、优化和持续监控工作中实施卓有成效的评估流程。

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本课程致力于为您提供所需的知识和工具，让您能够了解 MLOps 团队在部署和管理生成式 AI 模型以及探索 Vertex AI 如何帮助 AI 团队简化 MLOps 流程时面临的独特挑战，并帮助您在生成式 AI 项目中取得成功。

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完成中级技能徽章课程使用 Agent Platform 探索生成式 AI，展示自己在以下方面的技能： 文本生成技能、用于增强内容创作能力的图像和视频分析技能，以及在 Gemini API 中应用函数调用技术的技能。 了解如何运用先进的 Gemini 技术、探索多模态内容生成方法，并扩展 AI 赋能项目的功能。

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在本课程中，您将深入了解设计、构建和维护强大的 Gemini Enterprise for Customer Experience (GECX) 解决方案所必需的主题。做好准备，改变您对可能性的理解，并创建可提升客户满意度的架构。本课程旨在向您介绍 GECX 的架构。您将探索构建和实现对话式 AI 解决方案（包括关键架构组件和集成）的主要注意事项。您还将探索与 Gemini Enterprise Agent Platform 的集成，并大致了解 GECX 的主要功能。

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完成 在 Agent Platform 中设计提示入门技能徽章课程，展示以下方面的技能： Agent Platform 中的提示工程、图片分析和多模态生成式技术。探索如何编写有效的提示，指导生成式 AI 输出， 以及将 Gemini 模型应用于真实的营销场景。

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完成在 Google Cloud 上构建网站技能徽章课程，赢取入门级技能徽章。本课程以 Get Cooking in Cloud 系列视频为基础， 涵盖以下主题：在 Cloud Run 上部署网站在 Compute Engine 上托管 Web 应用在 Google Kubernetes Engine 上创建、部署和扩缩网站使用 Cloud Build 从单体式应用迁移到微服务架构

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大致了解 AI 智能体的概念。了解 AI 智能体如何通过自主行动和推理来解决复杂问题。您将探索技术架构（模型、工具和编排），该架构使智能体能够学习、规划，并为您实现目标。

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这是一节入门级微课程，旨在解释什么是负责任的 AI、它的重要性，以及 Google 如何在自己的产品中实现负责任的 AI。此外，本课程还介绍了 Google 的 7 个 AI 开发原则。

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这是一节入门级微学习课程，探讨什么是大型语言模型 (LLM)、适合的应用场景以及如何使用提示调整来提升 LLM 性能，还介绍了可以帮助您开发自己的 Gen AI 应用的各种 Google 工具。

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这是一节入门级微课程，旨在解释什么是生成式 AI、它的用途以及与传统机器学习方法的区别。该课程还介绍了可以帮助您开发自己的生成式 AI 应用的各种 Google 工具。

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Earn a skill badge by completing the Secure Workloads in Google Kubernetes Engine quest, where you learn about security at scale on Google Kubernetes Engine (GKE) including how to: migrate containers from virtual machines to Google Kubernetes Engine, restrict network connections in GKE using firewalls and Network Policies, use role-based access controls (RBAC) in GKE, use Binary Authorization for security controls of your images, secure applications in GKE using 3 access levels: host, network, Kubernetes API, and harden GKE cluster configurations. 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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完成为 Looker 信息中心和报告准备数据入门级技能徽章课程， 展现您在以下方面的技能：对数据进行过滤、排序和透视；将来自不同 Looker 探索的结果合并； 以及使用函数和运算符构建 Looker 信息中心和报告以用于数据分析和可视化。

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Earn a skill badge by completing the Build Interactive Apps with Google Assistant quest, where you will learn how to build Google Assistant applications, including how to: create an Actions project, integrate Dialogflow with an Actions project, test your application with Actions simulator, build an Assistant application with flash cards template, integrate customer MP3 files with your Assistant application, add Cloud Translation API to your Assistant application, and use APIs and integrate them into your applications. 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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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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完成设置 Google Cloud 网络课程，赢取技能徽章， 您将了解如何在 Google Cloud Platform 上执行基本的网络组建和管理任务 - 创建自定义网络、添加子网防火墙规则，然后创建虚拟机并测试 虚拟机之间相互通信时的延迟时间。

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完成在 Google Cloud 上实施云安全基础措施技能徽章中级课程， 展示自己在以下方面的技能：使用 Identity and Access Management (IAM) 创建和分配角色； 创建和管理服务账号；跨虚拟私有云 (VPC) 网络实现专用连接； 使用 Identity-Aware Proxy 限制应用访问权限； 使用 Cloud Key Management Service (KMS) 管理密钥和加密数据；创建专用 Kubernetes 集群。

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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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完成入门级使用 Google Cloud Observability 进行监控和记录技能徽章课程， 展示自己在以下方面的技能：监控 Compute Engine 中的虚拟机； 利用 Cloud Monitoring 监控多个项目；将监控和日志记录功能扩展到 Cloud Functions； 创建和发送自定义应用指标；以及根据自定义指标配置 Cloud Monitoring 提醒。

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完成在 Google Cloud 中实施 DevOps 工作流技能徽章中级课程， 展示您在以下方面的技能：创建和管理 git 代码库； 在 Google Kubernetes Engine (GKE) 上启动、管理和扩缩 Deployment； 设计 CI/CD 流水线架构，以自动构建容器映像并将其部署到 GKE。

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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 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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完成在 Google Cloud 上使用 Terraform 构建基础设施技能徽章中级课程， 展示您在以下方面的技能：在使用 Terraform 时遵循基础设施即代码 (IaC) 原则；利用 Terraform 配置 来预配和管理 Google Cloud 资源；管理有效状态（本地和远程）；以及将 Terraform 代码模块化，以方便重复使用和整理。

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完成开发 Google Cloud 网络课程，赢取技能徽章。在此课程中，您将学习 部署和监控应用的多种方法，包括执行以下任务的方法：探索 IAM 角色并添加/移除 项目访问权限、创建 VPC 网络、部署和监控 Compute Engine 虚拟机、 编写 SQL 查询、在 Compute Engine 中部署和监控虚拟机，以及使用 Kubernetes 通过多种部署方法部署应用。

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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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完成入门级技能徽章课程为 Compute Engine 实现云负载均衡，展示以下方面的技能： 在 Compute Engine 中创建和部署虚拟机 以及配置网络和应用负载均衡器。

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This advanced-level quest is unique amongst the other catalog 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 Data Engineer Certification. From Big Query, to Dataprep, to Cloud Composer, this quest is composed of specific labs that will put your Google Cloud data engineering knowledge to the test. Be aware that while practice with these labs will increase your skills and abilities, you will need other preparation, too. The exam is quite challenging and external studying, experience, and/or background in cloud data engineering is recommended. 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 the Engineer Data in the Google Cloud to receive an exclusive Google Cloud digital badge.

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This is the first of two Quests of hands-on labs is 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 first Quest, covering up through chapter 8, you are given the opportunity to practice all aspects of ingestion, preparation, processing, querying, exploring and visualizing data sets using Google Cloud tools and services.

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Machine Learning is one of the most innovative fields in technology, and the Google Cloud Platform 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 at scale and how to employ the advanced ML infrastructure available on Google Cloud.

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This is the second of two courses 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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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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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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完成入门级技能徽章课程在 Google Cloud 上为机器学习 API 准备数据，展示以下技能： 使用 Dataprep by Trifacta 清理数据、在 Dataflow 中运行数据流水线、在 Managed Service for Apache Spark 中创建集群和运行 Apache Spark 作业，以及调用机器学习 API，包括 Cloud Natural Language API、Google Cloud Speech-to-Text API 和 Video Intelligence API。

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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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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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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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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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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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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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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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大数据、机器学习和人工智能是当今计算领域的热门话题， 但这些领域的专业性很强，因而很难找到 入门资料。幸运的是，Google Cloud 在这些领域提供了方便用户使用的服务， 通过本入门级课程，您可以 开始学习使用 BigQuery、Cloud Speech API 和 Video Intelligence 等工具。

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In this introductory-level quest, you will learn the fundamentals of developing and deploying applications on the Google Cloud Platform. You will get hands-on experience with the Google App Engine framework by launching applications written in languages like Python, Ruby, and Java (just to name a few). You will see first-hand how straightforward and powerful GCP application frameworks are, and how easily they integrate with GCP database, data-loss prevention, and security services.

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Cloud SQL is a fully managed database service that stands out from its peers due to high performance, seamless integration, and impressive scalability. In this quest you will receive hands-on practice with the basics of Cloud SQL and quickly progress to advanced features, which you will apply to production frameworks and application environments. From creating instances and querying data with SQL, to building Deployment Manager scripts and connecting Cloud SQL instances with applications run on GKE containers, this quest will give you the knowledge and experience needed so you can start integrating this service right away.

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本课程最适合担任技术或财务职务 且负责管理 Google Cloud 费用的人员。您将学习如何设置 结算账号、组织资源以及管理结算访问权限。 在实操实验中，您将学习如何查看账单、使用 BigQuery 或 Google 表格分析结算数据， 以及使用 Data Studio 创建自定义结算信息中心。视频中提及的链接 可以在此其他资源文档中查看。

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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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如果您是一位入门级云开发者， 在学习了“Google Cloud 基础知识”课程之后，想要寻求真正的实操机会，这门课程就是您的不二之选。您将获得宝贵的实操经验， 通过多个实验深入探索 Cloud Storage 以及 Monitoring 和 Cloud Functions 等其他关键应用服务。您将掌握一系列宝贵技能， 在 Google Cloud 的任何计划中，这些技能都能发挥作用。

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在本入门级课程中，您将了解 Google Cloud 的基础工具和服务。此课程提供了可选视频， 旨在帮助您深入了解和回顾实验中涉及的概念。Google Cloud 基础知识是推荐给 Google Cloud 学员的第一门课程 - 即使您几乎没有云相关知识，也能从中获得实践 经验，并将其直接运用于您的首个 Google Cloud 项目。从编写 Cloud Shell 命令和部署您的第一个虚拟机，到在 Kubernetes Engine 上运行应用 或者使用负载均衡，“Google Cloud 基础知识”都是您了解该平台 基本功能的首选入门级课程。

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