Harvard Lim
成为会员时间：2020
白银联赛
34900 积分
在 Google Cloud 中实施 DevOps 工作流
Earned Aug 6, 2023 EDT
探索生成式 AI - Agent Platform
Earned Aug 5, 2023 EDT
在 Google Cloud 上实施云安全基础措施
Earned Jul 31, 2023 EDT
借助 Firebase 开发无服务器应用
Earned Jul 31, 2023 EDT
开发 Google Cloud 网络
Earned Jul 31, 2023 EDT
在 Google Cloud 上使用 Terraform 构建基础设施
Earned Jul 31, 2023 EDT
为 Compute Engine 实现云负载均衡
Earned Jul 17, 2023 EDT
在 Google Cloud 上为机器学习 API 准备数据
Earned Nov 13, 2021 EST
Scientific Data Processing
Earned Nov 13, 2021 EST
用 Database Migration Service 将 MySQL 数据迁移至 Cloud SQL
Earned Nov 8, 2021 EST
在 Looker 中应用高级 LookML 概念
Earned Nov 7, 2021 EST
为 Looker 信息中心和报告准备数据
Earned Nov 7, 2021 EST
Understanding LookML in Looker
Earned Nov 6, 2021 EDT
DEPRECATED BigQuery for Marketing Analysts
Earned Nov 5, 2021 EDT
DEPRECATED Applying BigQuery ML's Classification, Regression, and Demand Forecasting for Retail Applications
Earned Nov 4, 2021 EDT
通过 BigQuery ML 创建机器学习模型
Earned Nov 4, 2021 EDT
从 BigQuery 数据中挖掘数据洞见
Earned Nov 4, 2021 EDT
NCAA® March Madness®: Bracketology with Google Cloud
Earned Nov 4, 2021 EDT
DEPRECATED BigQuery Basics for Data Analysts
Earned Nov 4, 2021 EDT
面向机器学习的 BigQuery
Earned Oct 31, 2021 EDT
Machine Learning APIs
Earned Oct 31, 2021 EDT
基准：数据、机器学习和 AI
Earned Oct 31, 2021 EDT
DEPRECATED BigQuery for Data Warehousing
Earned Nov 14, 2020 EST
在 Cloud Data Fusion 上构建无代码流水线
Earned Nov 14, 2020 EST
DEPRECATED Explore Machine Learning Models with Explainable AI
Earned Nov 13, 2020 EST
Intro to ML: Image Processing
Earned Nov 13, 2020 EST
在 Google Cloud 上使用 Machine Learning API
Earned Nov 12, 2020 EST
使用 Google Cloud Observability 进行监控和记录
Earned Nov 12, 2020 EST
在 Google Cloud 上构建网站
Earned Nov 12, 2020 EST
DEPRECATED Google Cloud Solutions II: Data and Machine Learning
Earned Nov 11, 2020 EST
[Deprecated] Data Science on Google Cloud: Machine Learning
Earned Nov 11, 2020 EST
Automate Interactions with Contact Center AI
Earned Nov 11, 2020 EST
利用 BigQuery ML 构建预测模型时的数据工程处理
Earned Nov 11, 2020 EST
Data Catalog Fundamentals
Earned Nov 10, 2020 EST
[DEPRECATED] Secure Workloads in Google Kubernetes Engine
Earned Nov 9, 2020 EST
构建安全的 Google Cloud 网络
Earned Nov 9, 2020 EST
Getting Started with Go on Google Cloud
Earned Nov 9, 2020 EST
在 Google Cloud 上部署 Kubernetes 应用
Earned Nov 9, 2020 EST
在 Google Cloud 上设置应用开发环境
Earned Nov 9, 2020 EST
Anthos: Service Mesh
Earned May 15, 2020 EDT
[Deprecated] Advanced ML: ML Infrastructure
Earned May 14, 2020 EDT
DEPRECATED Creating with Google Maps
Earned May 13, 2020 EDT
Intermediate ML: TensorFlow on Google Cloud
Earned May 13, 2020 EDT
[DEPRECATED] Network Security by Palo Alto Networks
Earned May 11, 2020 EDT
Cloud Healthcare API
Earned May 11, 2020 EDT
[DEPRECATED] Asylo - Practical Confidential Computing with Enclaves
Earned May 10, 2020 EDT
DEPRECATED IoT in the Google Cloud
Earned May 10, 2020 EDT
Google Workspace 基本功能版
Earned May 10, 2020 EDT
[DEPRECATED] OK Google: Build Interactive Apps with Google Assistant
Earned May 9, 2020 EDT
机器学习简介：语言处理
Earned May 9, 2020 EDT
Google Kubernetes Engine Best Practices: Security
Earned May 9, 2020 EDT
DEPRECATED Applied Data: Blockchain
Earned May 7, 2020 EDT
Automate Deployment and Manage Traffic on a Google Cloud Network
Earned May 7, 2020 EDT
优化 Google Cloud 成本
Earned May 7, 2020 EDT
Google Cloud Solutions I: Scaling Your Infrastructure
Earned May 5, 2020 EDT
Deprecated Kubernetes Solutions
Earned May 4, 2020 EDT
DEPRECATED Application Development - Java
Earned May 3, 2020 EDT
Cloud SQL
Earned May 3, 2020 EDT
在 Google Cloud 中使用 Kubernetes
Earned May 2, 2020 EDT
DEPRECATED Application Development - Python
Earned May 2, 2020 EDT
Google Kubernetes Engine Best Practices
Earned May 2, 2020 EDT
基准：基础架构
Earned May 1, 2020 EDT
Google Cloud Run Serverless Workshop
Earned May 1, 2020 EDT
[DEPRECATED] Deploying Applications
Earned Apr 28, 2020 EDT
Baseline: Deploy & Develop
Earned Apr 27, 2020 EDT
了解自己的 Google Cloud 成本
Earned Apr 26, 2020 EDT
云工程
Earned Apr 25, 2020 EDT
DEPRECATED Cloud Architecture
Earned Apr 24, 2020 EDT
DevOps 基础知识
Earned Apr 21, 2020 EDT
安全与身份基础知识
Earned Apr 20, 2020 EDT
Google Cloud 基础知识
Earned Apr 19, 2020 EDT
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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探索生成式 AI - Agent Platform”课程包含一系列实验， 指导用户在 Google Cloud 平台上使用生成式 AI。通过这些实验，您将了解 如何在 Agent Platform 中使用 Gemini 模型。您还将了解提示设计、最佳实践， 以及如何使用生成式 AI 进行构思、文本分类、文本提取、文本摘要等。此外， 您将学习如何通过 Agent Platform 中的自定义训练来训练基础模型， 从而对其进行调优，以及如何将其部署到 Agent Platform 中的端点。
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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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完成借助 Firebase 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：借助 Firebase 设计无服务器 Web 应用架构以及构建无服务器 Web 应用； 利用 Firestore 管理数据库；利用 Cloud Build 自动完成部署流程； 以及将 Google 助理功能集成到您的应用中。
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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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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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Big data, machine learning, and scientific data? It sounds like the perfect match. In this course, you will get hands-on practice with GCP services like BigQuery, Managed Service for Apache Spark, and Tensorflow by applying them to use cases that employ real-life, scientific data sets. By getting experience with tasks like earthquake data analysis and satellite image aggregation, Scientific Data Processing will expand your skill set in big data and machine learning so you can start tackling your own problems across a spectrum of scientific disciplines.
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完成用 Database Migration Service 将 MySQL 数据迁移至 Cloud SQL 这一入门级的技能徽章课程，展示您在以下方面的技能： 使用 Database Migration Service 中提供的不同作业类型和连接选项，将 MySQL 数据迁移到 Cloud SQL； 以及在运行 Database Migration Service 作业时 迁移 MySQL 用户数据。
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在本课程中，您将获得在 Looker 中应用高级 LookML 概念 的实践经验。您将学习如何使用 Liquid 自定义和创建动态 维度和测量、创建动态 SQL 派生表和自定义原生 派生表，并运用扩展功能来模块化你的 LookML 代码
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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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In this quest, you will get hands-on experience with LookML in Looker. You will learn how to write LookML code to create new dimensions and measures, create derived tables and join them to Explores, filter Explores, and define caching policies in LookML.
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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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In this course you will learn how to use several BigQuery ML features to improve retail use cases. Predict the demand for bike rentals in NYC with demand forecasting, and see how to use BigQuery ML for a classification task that predicts the likelihood of a website visitor making a purchase.
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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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完成入门级技能徽章课程“从 BigQuery 数据中挖掘数据洞见”，展示您在以下方面的技能： 编写 SQL 查询、查询公共表、将示例数据加载到 BigQuery 中、 在 BigQuery 中使用查询验证器排查常见的语法错误，以及通过连接到 BigQuery 数据在 Data Studio 中 创建报告。
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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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想要仅使用 SQL 就能在几分钟内构建机器学习模型，而不是花费数小时？BigQuery 借助机器学习，数据分析师能够使用现有的 SQL 工具和技能创建、训练、评估机器学习模型，并使用这些模型进行预测， 从而实现机器学习的普及。在 本系列实验中，您将尝试不同的模型类型，并了解 如何构建出色的模型。
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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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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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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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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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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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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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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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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 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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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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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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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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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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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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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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完成在 Google Cloud 上部署 Kubernetes 应用技能徽章中级课程，展示您在以下方面的技能： 配置和构建 Docker 容器映像，创建和管理 Google Kubernetes Engine (GKE) 集群，利用 kubectl 实现高效 集群管理，以及按照稳健的持续交付 (CD) 实践部署 Kubernetes 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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This intermediate-level quest is unique among Qwiklabs quests. These labs have been curated to give operators hands-on practice with Anthos—a new, open application modernization platform on GCP. Anthos enables you to build and manage modern hybrid applications. Tasks include: installing service mesh, collecting telemetry, and securing your microservices with service mesh policies. This quest is composed of labs targeted to teach you everything you need to know to introduce service mesh, and Anthos, into your next hybrid cloud project.
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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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In this quest you will use several tools available in Google Cloud to manipulate data and create a Google Map - map location details to find subway stations or a business; use geocoding and Apps Script to send an email of a map; visualize data on a customized map; and build a server-side proxy to create a map on a mobile device.
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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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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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Cloud Healthcare API bridges the gap between care systems and applications built on Google Cloud. By supporting standards-based data formats and protocols of existing healthcare technologies, Cloud Healthcare API connects your data to advanced Google Cloud capabilities, including streaming data processing with Cloud Dataflow, scalable analytics with BigQuery, and machine learning with Cloud Machine Learning Engine. In this Quest you will use the Cloud Healthcare API to ingest and process data in the industry standard FHIR, HL7v2 and DICOM formats, train a TensorFlow model for prediction with FHIR data, and also gain practice with de-identification of datasets.
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This quest introduces you to Asylo (Greek for safe place), a developer framework and SDK for developing applications that run in trusted execution environments (TEEs).
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In this quest, you will learn about Google Cloud’s IoT Core service and its integration with other services like GCS, Dataprep, Stackdriver and Firestore. The labs in this quest use simulator code to mimic IOT devices and the learning here should empower you to implement the same streaming pipeline with real world IoT devices.
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Workspace 是 Google 的协作应用 平台，由 Google Cloud 提供。在本入门级课程中，您将 从用户角度对 Workspace 的核心应用进行实操练习。虽然 Workspace 包含的应用和工具组件远多于本课程涵盖的范围， 但您将实际操作主要应用：Gmail、日历、表格以及 以及其他一些应用。每个实验可在 10–15 分钟内完成，但课程预留了额外 时间，供您自主自由探索这些应用。
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With Google Assistant part of over a billion consumer devices, this quest teaches you how to build practical Google Assistant applications integrated with Google Cloud services via APIs. Example apps will use the Dialogflow conversational suite and the Actions and Cloud Functions frameworks. You will build 5 different applications that explore useful and fun tools you can extend on your own. No hardware required! These labs use the cloud-based Google Assistant simulator environment for developing and testing, but if you do have your own device, such as a Google Home or a Google Hub, additional instructions are provided on how to deploy your apps to your own hardware.
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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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Get Anthos Ready. This Google Kubernetes Engine-centric quest of best practice hands-on labs focuses on security at scale when deploying and managing production GKE environments -- specifically role-based access control, hardening, VPC networking, and binary authorization.
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Blockchain and related technologies, such as distributed ledger and distributed apps, are becoming new value drivers and solution priorities in many industries. In this course you will gain hands-on experience with distributed ledger and the exploration of blockchain datasets in Google Cloud. It brings the research and solution work of Google's Allen Day into self-paced labs for you to run and learn directly. Since this course uses advanced SQL in BigQuery, a SQL-in-BigQuery refresher lab is at the start.
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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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Google Cloud 结算和费用管理基础知识系列课程包含两部分内容， 本课是第二课。本课程最适合担任财务和/或 IT 职务 且负责优化所在组织的云基础设施的人员。 在这门课程中，您将学习多种控制和优化 Google Cloud 成本的方式， 包括设置预算和提醒、管理配额限制以及充分利用 承诺使用折扣。在实操实验中，您将练习使用各种 工具控制和优化 Google Cloud 成本，或者影响技术 团队应用成本优化最佳实践。
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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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Containerized applications have changed the game and are here to stay. With Kubernetes, you can orchestrate containers with ease, and integration with the Google Cloud Platform is seamless. In this advanced-level quest, you will be exposed to a wide range of Kubernetes use cases and will get hands-on practice architecting solutions over the course of 8 labs. From building Slackbots with NodeJS, to deploying game servers on clusters, to running the Cloud Vision API, Kubernetes Solutions will show you first-hand how agile and powerful this container orchestration system is.
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In this advanced-level quest, you will learn the ins and outs of developing GCP applications in Java. The first labs will walk you through the basics of environment setup and application data storage with Cloud Datastore. Once you have a handle on the fundamentals, you will get hands-on practice deploying Java applications on Kubernetes and App Engine (the latter is the same framework that powers Snapchat!) With specialized bonus labs that teach user authentication and backend service development, this quest will give you practical experience so you can start developing robust Java applications straight away.
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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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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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In this advanced-level quest, you will learn the ins and outs of developing GCP applications in Python. The first labs will walk you through the basics of environment setup and application data storage with Cloud Datastore. Once you have a handle on the fundamentals, you will get hands-on practice deploying Python applications on Kubernetes and App Engine (the latter is the same framework that powers Snapchat!) With specialized bonus labs that teach user authentication and backend service development, this quest will give you practical experience so you can start developing robust Python applications straight away.
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Get Anthos Ready. Demand for Google Kubernetes Engine is growing, and customers are looking to Google and its partners to provide in-depth technical knowledge. This first Google Kubernetes Engine-centric Quest of best practices hands-on labs will get you started containerizing to modernize in place , and then managing your deployed apps and services -- with monitoring, tracing, and logging.
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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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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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The Google Cloud Platform provides many different frameworks and options to fit your application’s needs. In this introductory-level quest, you will get plenty of hands-on practice deploying sample applications on Google App Engine. You will also dive into other web application frameworks like Firebase, Wordpress, and Node.js and see firsthand how they can be integrated with Google Cloud.
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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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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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在众多课程中，本入门课程独具特色。 这些实验经过精心设计，旨在让 IT 专业人员通过实践掌握 Google Cloud 认证 Associate Cloud Engineer 考核中的各项主题和服务内容。从 IAM 到网络组建和管理， 再到 Kubernetes Engine 部署，本课程将通过特定实验 检验您的 Google Cloud 知识掌握情况。请注意，虽然这些实操 实验有助于提升您的技能和能力，我们仍建议您同时查阅 考试指南和其他可用的备考资源。
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This fundamental-level quest is unique amongst the other quest 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 Cloud Architect Certification. From IAM, to networking, to Kubernetes engine deployment, this quest is composed of specific labs that will put your Google Cloud knowledge to the test. Be aware that while practice with these labs will increase your skills and abilities, we recommend that you also review the exam guide and other available preparation resources.
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通过 DevOps 获得 竞争优势。DevOps 是一种组织和文化运动，旨在加快软件交付速度， 提高服务可靠性，并在软件利益相关方之间 建立共享所有权。在本课程中，您将学习如何使用 Google Cloud 提高 软件交付能力，确保较高的速度、稳定性、可用性和安全性。 DevOps Research and Assessment 已加入 Google Cloud。您的团队表现如何？参加 这个包含 5 道选择题的测验，来一探究竟吧！
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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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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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