Chew Tzi Hwee
成为会员时间：2019
白银联赛
7700 积分
机器学习简介：语言处理
Earned Nov 13, 2020 EST
Intro to ML: Image Processing
Earned Nov 13, 2020 EST
DEPRECATED Google Cloud Solutions II: Data and Machine Learning
Earned Nov 13, 2020 EST
[Deprecated] Data Science on Google Cloud: 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
构建安全的 Google Cloud 网络
Earned Nov 12, 2020 EST
DEPRECATED Explore Machine Learning Models with Explainable AI
Earned Nov 10, 2020 EST
在 Google Cloud 上为机器学习 API 准备数据
Earned Nov 10, 2020 EST
DEPRECATED BigQuery for Data Warehousing
Earned Nov 9, 2020 EST
Data Catalog Fundamentals
Earned Nov 9, 2020 EST
从 BigQuery 数据中挖掘数据洞见
Earned Nov 9, 2020 EST
使用 Google Cloud Observability 进行监控和记录
Earned Nov 9, 2020 EST
Getting Started with Go on Google Cloud
Earned Oct 29, 2020 EDT
在 Google Cloud 上构建网站
Earned Oct 28, 2020 EDT
在 Google Cloud 上部署 Kubernetes 应用
Earned Oct 22, 2020 EDT
在 Google Cloud 上设置应用开发环境
Earned Oct 22, 2020 EDT
Cloud Development
Earned May 1, 2020 EDT
DEPRECATED Application Development - Python
Earned May 1, 2020 EDT
了解自己的 Google Cloud 成本
Earned May 1, 2020 EDT
Google Cloud Run Serverless Workshop
Earned Apr 30, 2020 EDT
Baseline: Deploy & Develop
Earned Apr 29, 2020 EDT
Google Kubernetes Engine Best Practices
Earned Apr 28, 2020 EDT
[Deprecated] Cloud Logging
Earned Apr 27, 2020 EDT
Deprecated Kubernetes Solutions
Earned Apr 27, 2020 EDT
基准：基础架构
Earned Apr 26, 2020 EDT
VM Migration
Earned Apr 25, 2020 EDT
DevOps 基础知识
Earned Apr 24, 2020 EDT
在 Google Cloud 中使用 Kubernetes
Earned Apr 23, 2020 EDT
Cloud SQL
Earned Apr 22, 2020 EDT
优化 Google Cloud 成本
Earned Apr 22, 2020 EDT
Security on AWS
Earned Apr 15, 2020 EDT
Google Cloud 基础知识
Earned Apr 12, 2020 EDT
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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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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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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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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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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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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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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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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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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The hands-on labs in this Quest are structured to give experienced app developers hands-on practice with the state-of-the-art developing applications in Google Cloud. The topics align with the Google Cloud Certified Professional Cloud Developer Certification. These labs follow the sequence of activities needed to create and deploy an app in Google Cloud from beginning to end. Be aware that while practice with these labs will increase your skills and abilities, it is recommended that you also review the exam guide and other available preparation resources.
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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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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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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 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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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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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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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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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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Google Cloud’s four step structured Cloud Migration Path Methodology provides a defined and repeatable path for users to follow when migrating and modernizing Virtual Machines. In this quest, you will get hands-on practice with Google’s current solution set for VM assessment, planning, migration, and modernization. You will start by analyzing your lab environment and building assessment reports with CloudPhysics and StratoZone, then build a landing zone within Google Cloud leveraging Terraform’s infrastructure-as-code templates, next you will manually transform a two-tier application into a cloud-native workload running on Kubernetes, and finally, transform a VM workload into Kubernetes with Migrate for Anthos and migrate a VM between cloud environments.
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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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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 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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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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This quest is designed to teach you how to apply AWS Identity and Access Management, in concert with several other AWS Services, to address real-world application and service security management scenarios.
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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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