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Google Cloud 콘솔에서 기술 적용

Muhamad Efendi

회원 가입일: 2019

실버 리그

10100포인트
Google Kubernetes Engine 비용 최적화 Earned 3월 20, 2021 EDT
Google Cloud 네트워크 개발 Earned 3월 19, 2021 EDT
Google Cloud에서 DevOps 워크플로 구현 Earned 3월 19, 2021 EDT
Compute Engine에서 Cloud Load Balancing 구현하기 Earned 3월 19, 2021 EDT
Google Cloud Observability로 모니터링 및 로깅 Earned 3월 19, 2021 EDT
Managing Cloud Infrastructure with Terraform Earned 3월 14, 2021 EDT
DEPRECATED Google Cloud's Operations Suite on GKE Earned 3월 14, 2021 EDT
Anthos: Service Mesh Earned 3월 13, 2021 EST
Google Cloud 앱 개발 환경 설정 Earned 3월 12, 2021 EST
DEPRECATED Cloud Architecture Earned 3월 11, 2021 EST
Firebase로 서버리스 앱 개발 Earned 3월 11, 2021 EST
DEPRECATED Websites and Web Applications Earned 5월 21, 2020 EDT
DEPRECATED Application Development - Java Earned 5월 6, 2020 EDT
Deprecated Kubernetes Solutions Earned 5월 6, 2020 EDT
[DEPRECATED] Deploying Applications Earned 5월 6, 2020 EDT
Cloud Logging Earned 5월 5, 2020 EDT
DEPRECATED Application Development - Python Earned 5월 5, 2020 EDT
Cloud Development Earned 5월 5, 2020 EDT
Data Science on Google Cloud: Machine Learning Earned 5월 4, 2020 EDT
DEPRECATED Google Cloud Solutions II: Data and Machine Learning Earned 5월 4, 2020 EDT
Advanced ML: ML Infrastructure Earned 5월 3, 2020 EDT
Intermediate ML: TensorFlow on Google Cloud Earned 5월 2, 2020 EDT
Intro to ML: Image Processing Earned 5월 2, 2020 EDT
머신러닝 소개: 언어 처리 Earned 5월 2, 2020 EDT
NCAA® March Madness®: Bracketology with Google Cloud Earned 3월 12, 2020 EDT
Scientific Data Processing Earned 3월 12, 2020 EDT
[DEPRECATED] Data Engineering Earned 3월 7, 2020 EST
Google Cloud 비용 이해하기 Earned 11월 24, 2019 EST
Google Developer Essentials Earned 11월 19, 2019 EST
DevOps Essentials Earned 11월 18, 2019 EST
[DEPRECATED] OK Google: Build Interactive Apps with Google Assistant Earned 11월 18, 2019 EST
Baseline: Deploy & Develop Earned 11월 17, 2019 EST
VM Migration Earned 11월 16, 2019 EST
Google Cloud의 Kubernetes Earned 11월 14, 2019 EST
보안 및 ID 기초 Earned 11월 4, 2019 EST
DEPRECATED BigQuery Basics for Data Analysts Earned 10월 30, 2019 EDT
기준: 데이터, ML, AI Earned 10월 30, 2019 EDT
기준: 인프라 Earned 10월 27, 2019 EDT
Google Cloud 필수 정보 Earned 10월 27, 2019 EDT

중급 Google Kubernetes Engine 비용 최적화 기술 배지 과정을 완료하여 멀티 테넌트 클러스터의 생성 및 관리, 리소스 사용량의 네임스페이스별 모니터링, 효율을 위한 클러스터 및 포드 자동 확장 구성, 최적의 리소스 배포를 위한 부하 분산 설정, 애플리케이션 상태와 비용 효율을 위한 활성 프로브 및 준비 프로브 구현 작업과 관련된 기술 역량을 입증하세요.

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Google Cloud 네트워크 개발 과정을 완료하고 기술 배지를 획득하세요. 이 과정에서는 IAM 역할 탐색 및 프로젝트 액세스 권한 추가/삭제, VPC 네트워크 생성, Compute Engine VM 배포 및 모니터링, SQL 쿼리 작성, Compute Engine에서 VM 배포 및 모니터링, Kubernetes를 여러 배포 접근 방식과 함께 사용하여 애플리케이션을 배포하는 등의 다양한 애플리케이션 배포 및 모니터링 방법을 배울 수 있습니다.

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중급 Google Cloud에서 DevOps 워크플로 구현 기술 배지 과정을 완료하여 Cloud Source Repositories로 Git 저장소 만들기, Google Kubernetes Engine(GKE)에서 배포 실행, 관리, 확장, 그리고 컨테이너 이미지 빌드 및 GKE로의 배포를 자동화하는 CI/CD 파이프라인 설계 등에 관한 기술을 입증하세요.

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초급 Compute Engine에서 Cloud Load Balancing 구현하기 기술 배지 과정을 완료하여 Compute Engine에서 가상 머신 만들기 및 배포, 네트워크 및 애플리케이션 부하 분산기 구성과 관련된 기술 역량을 입증하세요.

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초급 Google Cloud Observability로 모니터링 및 로깅 기술 배지를 획득하여 Compute Engine에서 가상 머신 모니터링, Cloud Monitoring을 활용한 다중 프로젝트 감독, Cloud Functions로 모니터링 및 로깅 기능 확장, 커스텀 애플리케이션 측정항목 생성 및 전송, 커스텀 측정항목을 기반으로 Cloud Monitoring 알림 구성 등의 기술을 입증하세요.

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In this Quest, the experienced user of Google Cloud will learn how to describe and launch cloud resources with Terraform, an open source tool that codifies APIs into declarative configuration files that can be shared amongst team members, treated as code, edited, reviewed, and versioned. In these nine hands-on labs, you will work with example templates and understand how to launch a range of configurations, from simple servers, through full load-balanced applications.

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In this fundamental-level course, you will learn the ins and outs of Google Cloud's operations suite running on Google Kubernetes Engine, an important service for generating insights into the health of your applications. It provides a wealth of information in application monitoring, report logging, and diagnoses. The labs in this course will give you hands-on practice with and will teach you how to monitor virtual machines, generate logs and alerts, and create custom metrics for application data. It is recommended that the students have at least earned a Badge by completing the Google Cloud Essentials course. Additional lab experience with the labs in the Baseline - Infrastructure course will also be useful. Looking for a hands-on challenge lab to demonstrate your skills and validate your knowledge? On completing this course, enroll in and finish the additional challenge lab at the end of this course to receive an exclusive Google Cloud digital badge.

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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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Google Cloud 앱 개발 환경 설정 과정을 완료하여 기술 배지를 획득하세요. Cloud Storage, Identity and Access Management, Cloud Functions, Pub/Sub의 기본 기능을 사용하여 스토리지 중심 클라우드 인프라를 구축하고 연결하는 방법을 배울 수 있습니다.

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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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중급 Firebase로 서버리스 앱 개발 기술 배지 과정을 완료하여 Firebase를 사용한 서버리스 웹 애플리케이션 설계 및 빌드, 데이터베이스 관리에 Firestore 활용, Cloud Build를 사용하여 배포 프로세스 자동화, 애플리케이션에 Google 어시스턴트 기능 통합 등에 관한 기술을 입증하세요.

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When it comes to hosting websites and web applications, you want a framework that’s robust, fast, and secure. By choosing the Google Cloud Platform, you will have all of those needs covered. In this fundamental-level quest, you will get hands-on practice with GCPs key infrastructure and computing services for the web. From deploying your first web app, to integrating Cloud SQL with Ruby on Rails, to mapping the NYC subway system on App Engine, you will learn all the skills needed to harness GCPs web hosting power.

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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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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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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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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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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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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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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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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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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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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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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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모두 알다시피 머신러닝은 빠르게 성장 중인 기술 분야 중 하나입니다. Google Cloud Platform(GCP)은 이러한 발전을 촉진하는 데 중요한 역할을 했습니다. GCP는 다양한 API를 통해 거의 모든 머신러닝 작업에 적합한 도구를 제공합니다. 이 초급 과정에서는 실무형 실습을 통해 머신러닝을 언어 처리에 적용하는 방법을 알아봅니다. 실습에 참여하여 텍스트에서 항목을 추출하고 감정 및 구문 분석을 수행하며 스크립트 작성에 Speech-to-Text API를 사용해 보세요.

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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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Big data, machine learning, and scientific data? It sounds like the perfect match. In this advanced-level quest, you will get hands-on practice with GCP services like Big Query, Dataproc, 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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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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이 과정은 Google Cloud 비용 관리를 담당하는 기술 또는 재무 관련 직무에 적합합니다. 결제 계정 설정 방법, 리소스 정리 방법, 결제 액세스 권한 관리 방법을 알아봅니다. 실무형 실습에서는 인보이스를 보는 방법, Billing 보고서를 통해 Google Cloud 비용을 추적하는 방법, BigQuery 또는 Google Sheets를 사용하여 결제 데이터를 분석하는 방법, Looker Studio를 사용하여 커스텀 결제 대시보드를 만드는 방법을 살펴봅니다. 동영상의 참조 링크는 이 추가 리소스 문서에서 액세스할 수 있습니다.

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This introductory-level quest shows application developers how the Google Cloud ecosystem could help them build secure, scalable, and intelligent cloud native applications. You learn how to develop and scale applications without setting up infrastructure, run data analytics, gain insights from data, and develop with pre-trained ML APIs to leverage machine learning even if you are not a Machine Learning expert. You will also experience seamless integration between various Google services and APIs to create intelligent apps.

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Obtain a competitive advantage through DevOps. DevOps is an organizational and cultural movement that aims to increase software delivery velocity, improve service reliability, and build shared ownership among software stakeholders. In this course you will learn how to use Google Cloud to improve the speed, stability, availability, and security of your software delivery capability. DevOps Research and Assessment has joined Google Cloud. How does your team measure up? Take this five question multiple-choice quiz and find out!

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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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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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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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Kubernetes는 가장 인기 있는 컨테이너 조정 시스템이며, Google Kubernetes Engine은 Google Cloud에서 관리형 Kubernetes 배포를 지원하도록 특별히 설계되었습니다. 이 고급 과정에서는 Docker 이미지, 컨테이너를 구성하고 완전한 Kubernetes Engine 애플리케이션을 배포하는 실무형 실습을 진행합니다. 이 과정에서는 컨테이너 조정을 자체 워크플로에 통합하는 데 필요한 실용적인 기술을 알려드립니다. 기술을 입증하고 지식을 확인할 실무형 챌린지 실습을 찾고 계신가요? 이 과정을 마친 후 추가로 챌린지 실습을 완료하여 전용 Google Cloud 디지털 배지를 받으세요. 이 챌린지 실습은 Google Cloud에서 Kubernetes 애플리케이션 배포하기 과정이 끝나면 제공됩니다.

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Google Cloud 서비스는 보안에 있어 타협하지 않습니다. Google Cloud에서 프로젝트 전반의 보안과 ID를 보장하는 전용 도구를 개발했습니다. 이 초급 과정에서는 실무형 실습을 통해 Google Cloud의 Identity and Access Management(IAM) 서비스에 대해 알아봅니다. 이 서비스는 사용자 및 가상 머신 계정을 관리할 때 사용됩니다. VPC 및 VPN을 프로비저닝하여 네트워크 보안을 경험하고 보안 위협 및 데이터 손실 방지를 위해 사용할 수 있는 도구를 알아봅니다.

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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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빅데이터, 머신러닝, 인공지능은 오늘날 인기 있는 컴퓨팅 관련 주제이지만 매우 전문화된 분야이기 때문에 초급용 자료를 구하기 어렵습니다. 다행히도 Google Cloud는 이러한 분야에서 사용자 친화적인 서비스를 제공하며 초급 과정을 통해 학습자에게 BigQuery, Cloud Speech API, Video Intelligence와 같은 도구를 사용해 시작할 기회를 제공합니다.

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이 과정은 Google Cloud 기본 개념 과정 이상의 지식을 얻기 위해 실무형 실습을 찾는 초보 클라우드 개발자에게 도움이 됩니다. 실습을 통해 Cloud Storage와 Monitoring 및 Cloud Functions 등 기타 주요 애플리케이션 서비스를 자세히 살펴보며 실무 경험을 쌓게 됩니다. 모든 Google Cloud 이니셔티브에 적용할 수 있는 유용한 기술을 개발할 수 있습니다.

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이 초급 과정에서는 Google Cloud의 기본 도구 및 서비스를 직접 사용해 보는 실무형 실습을 진행합니다. 선택사항으로 제공되는 동영상에서는 실습에서 다룬 개념을 자세히 살펴보고 복습합니다. Google Cloud 필수 정보는 Google Cloud 학습자에게 추천되는 첫 번째 과정입니다. 클라우드에 대한 사전 지식이 거의 없거나 전혀 없더라도 첫 Google Cloud 프로젝트에 적용할 수 있는 실무 경험을 쌓을 수 있습니다. Cloud Shell 명령어 작성, 첫 번째 가상 머신 배포, Kubernetes Engine에서의 애플리케이션 실행, 부하 분산 등 Google Cloud 필수 정보에서는 플랫폼의 기본 기능을 소개합니다.

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