Google Workspace User and Resource Management
Earned Jul 24, 2023 EDT
Securing your Network with Cloud Armor
Earned Jul 13, 2023 EDT
Google Kubernetes Engine Best Practices: Security
Earned Jul 13, 2023 EDT
Mitigating Security Vulnerabilities on Google Cloud
Earned Jul 12, 2023 EDT
Security Best Practices in Google Cloud
Earned Jul 11, 2023 EDT
Enterprise Database Migration
Earned Jun 30, 2023 EDT
透過 Google Cloud Observability 監控及記錄系統狀態
Earned Jun 20, 2023 EDT
Get Started with Looker
Earned Jun 20, 2023 EDT
Managing Security in Google Cloud
Earned Jun 19, 2023 EDT
Preparing for Your Professional Cloud Security Engineer Journey
Earned Jun 16, 2023 EDT
Machine Learning Operations (MLOps): Getting Started
Earned Jun 8, 2023 EDT
Recommendation Systems on Google Cloud
Earned Jun 8, 2023 EDT
Natural Language Processing on Google Cloud
Earned Jun 8, 2023 EDT
Computer Vision Fundamentals with Google Cloud
Earned Jun 7, 2023 EDT
Production Machine Learning Systems
Earned Jun 5, 2023 EDT
Machine Learning in the Enterprise
Earned May 31, 2023 EDT
Feature Engineering
Earned May 30, 2023 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud
Earned May 29, 2023 EDT
使用 BigQuery ML 為預測模型進行資料工程
Earned May 28, 2023 EDT
Launching into Machine Learning
Earned May 27, 2023 EDT
How Google Does Machine Learning
Earned May 26, 2023 EDT
透過 BigQuery 建構資料倉儲
Earned May 22, 2023 EDT
在 Google Cloud 為機器學習 API 準備資料
Earned May 22, 2023 EDT
Serverless Data Processing with Dataflow: Operations
Earned May 17, 2023 EDT
Serverless Data Processing with Dataflow: Develop Pipelines
Earned May 17, 2023 EDT
Serverless Data Processing with Dataflow: Foundations
Earned May 14, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned May 12, 2023 EDT
Building Resilient Streaming Systems on Google Cloud Platform
Earned May 12, 2023 EDT
Building Batch Data Pipelines on Google Cloud
Earned May 11, 2023 EDT
Modernizing Data Lakes and Data Warehouses with Google Cloud
Earned May 10, 2023 EDT
Preparing for your Professional Data Engineer Journey
Earned May 10, 2023 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned May 8, 2023 EDT
Gmail
Earned Apr 3, 2023 EDT
Certification Learning Path: Professional Cloud DevOps Engineer
Earned Mar 25, 2023 EDT
Measure Site Reliability using Cloud Operations Suite
Earned Mar 25, 2023 EDT
在 Google Cloud 實作 DevOps 工作流程
Earned Mar 19, 2023 EDT
DEPRECATED Cloud Operations and Service Mesh with Anthos
Earned Mar 17, 2023 EDT
Getting Started with Terraform for Google Cloud
Earned Mar 16, 2023 EDT
Developing a Google SRE Culture
Earned Mar 16, 2023 EDT
Certification Learning Path: Professional Cloud Network Engineer
Earned Mar 16, 2023 EDT
建構安全的 Google Cloud 網路
Earned Mar 16, 2023 EDT
在 Google Cloud 實作 Cloud 安全防護措施：基礎知識
Earned Mar 15, 2023 EDT
Preparing for Your Professional Cloud Network Engineer Journey
Earned Mar 15, 2023 EDT
Networking in Google Cloud: Fundamentals
Earned Mar 15, 2023 EDT
DEPRECATED Network Performance and Optimization
Earned Mar 12, 2023 EST
Automate Deployment and Manage Traffic on a Google Cloud Network
Earned Mar 7, 2023 EST
Networking in Google Cloud: Routing and Addressing
Earned Mar 7, 2023 EST
Preparing for Your Associate Cloud Engineer Journey
Earned Feb 28, 2023 EST
Certification Learning Path: Professional Cloud Architect
Earned Feb 27, 2023 EST
雲端架構：設計、實作與管理
Earned Feb 27, 2023 EST
設定 Google Cloud 網路
Earned Feb 27, 2023 EST
Google Kubernetes Engine 成本效益最佳化
Earned Feb 27, 2023 EST
在 Google Cloud 使用 Terraform 建構基礎架構
Earned Feb 26, 2023 EST
建立 Google Cloud 網路
Earned Feb 22, 2023 EST
在 Google Cloud 設定應用程式開發環境
Earned Feb 21, 2023 EST
在 Compute Engine 實作負載平衡功能
Earned Feb 21, 2023 EST
Architecting with Google Kubernetes Engine: Workloads
Earned Feb 20, 2023 EST
Architecting with Google Kubernetes Engine: Production
Earned Feb 20, 2023 EST
Preparing for your Professional Cloud Architect Journey
Earned Feb 15, 2023 EST
Architecting with Google Kubernetes Engine: Foundations - 繁體中文
Earned Feb 14, 2023 EST
可靠的 Google Cloud 基礎架構：設計與程序
Earned Feb 14, 2023 EST
Logging and Monitoring in Google Cloud
Earned Feb 13, 2023 EST
重要的 Google Cloud 基礎架構：核心服務
Earned Feb 13, 2023 EST
重要的 Google Cloud 基礎架構：基本概念
Earned Feb 11, 2023 EST
彈性的 Google Cloud 基礎架構：資源調度與自動化
Earned Feb 10, 2023 EST
使用 Firebase 開發無伺服器應用程式
Earned Feb 1, 2023 EST
運用 Cloud Run 開發無伺服器應用程式
Earned Feb 1, 2023 EST
開始使用 Google Kubernetes Engine
Earned Jan 31, 2023 EST
Getting Started With Application Development
Earned Jan 30, 2023 EST
Google Cloud 基礎知識：核心基礎架構
Earned Jan 23, 2023 EST
Scaling with Google Cloud Operations
Earned Jan 11, 2023 EST
Modernize Infrastructure and Applications with Google Cloud
Earned Jan 11, 2023 EST
Exploring Data Transformation with Google Cloud
Earned Jan 11, 2023 EST
Digital Transformation with Google Cloud
Earned Jan 11, 2023 EST
This course introduces you to fundamentals, practices, capabilities and tools applicable to modern cloud-native application development using Google Cloud Run. Through a combination of lectures, hands-on labs, and supplemental materials, you will learn how to on Google Cloud using Cloud Run.design, implement, deploy, secure, manage, and scale applications
Course four of the Anthos series prepares students to consider multiple approaches for modernizing applications and services within Anthos environments. Topics include optimizing workloads on serverless platforms and migrating workloads to Anthos. This course is a continuation of course three, Anthos on Bare Metal, and assumes direct experience with the topics covered in that course.
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In this course, application developers learn how to design and develop cloud-native applications that seamlessly integrate components from the Google Cloud ecosystem. Through a combination of presentations, demos, and hands-on labs, participants learn how to create repeatable deployments by treating infrastructure as code, choose the appropriate application execution environment for an application, and monitor application performance.
Completing one version of each lab is required. Each lab is available in Node.js. In most cases, the same labs are also provided in Python or Java. You may complete each lab in whichever language you prefer.
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In this course, application developers learn how to design and develop cloud-native applications that seamlessly integrate managed services from Google Cloud. Through a combination of presentations, demos, and hands-on labs, participants learn how to develop more secure applications, implement federated identity management, and integrate application components by using messaging, event-driven processing, and API gateways. Completing one version of each lab is required. Each lab is available in Node.js. In most cases, the same labs are also provided in Python or Java. You may complete each lab in whichever language you prefer. This is the second course of the Developing Applications with Google Cloud series. After completing this course, enroll in the App Deployment, Debugging, and Performance course.
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完成「運用 BigQuery ML 建立機器學習模型」技能徽章中階課程，即可證明您具備下列技能： 可使用 BigQuery ML 建立及評估機器學習模型，並根據資料進行預測。 「技能徽章」是 Google Cloud 核發的獨家數位徽章， 用於肯定您在 Google Cloud 產品和服務方面的精熟技能， 代表您已通過測驗，能在互動式實作環境中應用相關知識。完成 本課程及結業評量挑戰實驗室，即可取得技能徽章 並與他人分享。
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Complete the introductory Create and Manage AlloyDB Instances skill badge to demonstrate skills in the following: performing core AlloyDB operations and tasks, migrating to AlloyDB from PostgreSQL, administering an AlloyDB database, and accelerating analytical queries using the AlloyDB Columnar Engine.
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Complete the introductory Migrate MySQL data to Cloud SQL using Database Migration Services skill badge to demonstrate skills in the following: migrating MySQL data to Cloud SQL using different job types and connectivity options available in Database Migration Service and migrating MySQL user data when running Database Migration Service jobs. 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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Complete the introductory Create and Manage Bigtable Instances skill badge to demonstrate skills in the following: creating instances, designing schemas, querying data, and performing administrative tasks in Bigtable including monitoring performance and configuring node autoscaling and replication.
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In this course, we introduce you to Google Meet, Google’s video conference software included with Google Workspace.
You learn how to create and manage video conference meetings using Google Meet. You explore different ways to open Google Meet and add people to a video conference. You also learn how to join meetings from different sources like calendar events or meeting links.
We discuss how Google Meet can help you better communicate, exchange ideas, and share resources with your team wherever they are. You learn how to customize the Google Meet environment to fit your needs and how to effectively use chat messages during a video conference. You also explore different ways to share resources, such as by using calendar invites or attachments.
You learn about using host controls in Google Meet to manage participants and utilize interactive moderation features. You also learn how to record and live stream video conferences.
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With Google Slides, you can create and present professional presentations for sales, projects, training modules, and much more.
Google Slides presentations are stored safely in the cloud. You build presentations right in your web browser—no special software is required. Even better, multiple people can work on your slides at the same time, you can see people’s changes as they make them, and every change is automatically saved.
You will learn how to open Google Slides, create a blank presentation, and create a presentation from a template. You will explore themes, layout options, and how to add and format content, and speaker notes in your presentations.
You will learn how to enhance your slides by adding tables, images, charts, and more. You will also learn how to use slide transitions and object animations in your presentation for visual effects.
We will discuss how to organize slides and explore some of the options, including duplicating and ordering your slides, importi…
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With Google Docs, your documents are stored in the cloud, and you can access them from any computer or device. You create and edit documents right in your web browser; no special software is required. Even better, multiple people can work at the same time, you can see people’s changes as they make them, and every change is saved automatically.
In this course, you will learn how to open Google Docs, create and format a new document, and apply a template to a new document.
You will learn how to enhance your documents using a table of contents, headers and footers, tables, drawings, images, and more.
You will learn how to share your documents with others. We will discuss your sharing options and examine collaborator roles and permissions. You will learn how to manage versions of your documents.
Google Docs allows you to work in real time with others on the same document. You will learn how to create and manage comments and action items in your documents.
We will review a few of the G…
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With Google Calendar, you can quickly schedule meetings and events and create tasks, so you always know what’s next. Google Calendar is designed for teams, so it’s easy to share your schedule with others and create multiple calendars that you and your team can use together.
In this course, you’ll learn how to create and manage Google Calendar events. You will learn how to update an existing event, delete and restore events, and search your calendar.
You will understand when to apply different event types such as tasks and appointment schedules.
You will explore the Google Calendar settings that are available for you to customize Google Calendar to suit your way of working.
During the course you will learn how to create additional calendars, share your calendars with others, and access other calendars in your organization.
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Google Drive is Google’s cloud-based file storage service. Google Drive lets you keep all your work in one place, view different file formats without the need for additional software, and access your files from any device.
In this course, you will learn how to navigate your Google Drive. You will learn how to upload files and folders and how to work across file types. You will also learn how you can easily view, arrange, organize, modify, and remove files in Google Drive.
Google Drive includes shared drives. You can use shared drives to store, search, and access files with a team. You will learn how to create a new shared drive, add and manage members, and manage the shared drive content.
Google Workspace is synonymous with collaboration and sharing. You will explore the sharing options available to you in Google Drive, and you will learn about the various collaborator roles and permissions that can be assigned.
You’ll also explore ways to ensure consistency and save time…
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This course builds on some of the concepts covered in the earlier Google Sheets course.
In this course, you will learn how to apply and customize themes In Google Sheets, and explore conditional formatting options.
You will learn about some of Google Sheets’ advanced formulas and functions. You will explore how to create formulas using functions, and you will also learn how to reference and validate your data in a Google Sheet.
Spreadsheets can hold millions of numbers, formulas, and text. Making sense of all of that data can be difficult without a summary or visualization. This course explores the data visualization options in Google Sheets, such as charts and pivot tables.
Google Forms are online surveys used to collect data and provide the opportunity for quick data analysis. You will explore how Forms and Sheets work together by connecting collected Form data to a spreadsheet, or by creating a Form from an existing spreadsheet.
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In this course we will introduce you to Google Sheets, Google’s cloud-based spreadsheet software, included with Google Workspace.
With Google Sheets, you can create and edit spreadsheets directly in your web browser—no special software is required.
Multiple people can work simultaneously, you can see people’s changes as they make them, and every change is saved automatically.
You will learn how to open Google Sheets, create a blank spreadsheet, and create a spreadsheet from a template. You will add, import, sort, filter and format your data using Google Sheets and learn how to work across different file types.
Formulas and functions allow you to make quick calculations and better use your data. We will look at creating a basic formula, using functions, and referencing data. You will also learn how to add a chart to your spreadsheet.
Google Sheets spreadsheets are easy to share. We will look at the different ways you can share with others. We will also discuss how to track changes…
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Earn a skill badge by completing the Configure your Workplace: Google Workspace for IT Admins quest, where you will get try out the Admin role for Workspace and learn to provision Groups, manage applications, security, and manage Meet. 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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Planning for a Google Workspace Deployment is the final course in the Google Workspace Administration series.
In this course, you will be introduced to Google's deployment methodology and best practices. You will follow Katelyn and Marcus as they plan for a Google Workspace deployment at Cymbal. They'll focus on the core technical project areas of provisioning, mail flow, data migration, and coexistence, and will consider the best deployment strategy for each area.
You will also be introduced to the importance of Change Management in a Google Workspace deployment, ensuring that users make a smooth transition to Google Workspace and gain the benefits of work transformation through communications, support, and training.
This course covers theoretical topics, and does not have any hands on exercises. If you haven’t already done so, please cancel your Google Workspace trial now to avoid any unwanted charges.
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This course equips learners with skills to govern data within their Google Workspace environment. Learners will explore data loss prevention rules in Gmail and Drive to prevent data leakage. They will then learn how to use Google Vault for data retention, preservation, and retrieval purposes. Next, they will learn how to configure data regions and export settings to align with regulations. Finally, learners will discover how to classify data using labels for enhanced organization and security.
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This course empowers learners to secure their Google Workspace environment. Learners will implement strong password policies and two-step verification to govern user access. They will then utilize the security investigation tool to proactively identify and respond to security risks. Next, they will manage third-party app access and mobile devices to ensure security. Finally, learners will enforce email security and compliance measures to protect organizational data.
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This course was designed to give learners a comprehensive understanding of Google Workspace core services. Learners will explore enabling, disabling, and configuring settings for these services, including Gmail, Calendar, Drive, Meet, Chat, and Docs. Next, they'll learn how to deploy and manage Gemini to empower their users. Finally, learners will examine use cases for AppSheet and Apps Script to automate tasks and extend the functionality of Google Workspace applications.
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Complete the introductory Create and Manage Cloud Spanner Instances skill badge to demonstrate skills in the following: creating and interacting with Cloud Spanner instances and databases; loading Cloud Spanner databases using various techniques; backing up Cloud Spanner databases; defining schemas and understanding query plans; and deploying a Modern Web App connected to a Cloud Spanner instance.
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This course was designed to provide an understanding of user and resource management in Google Workspace. Learners will explore the configuration of organizational units to align with their organization's needs. Additionally, learners will discover how to manage various types of Google Groups. They will also develop expertise in managing domain settings within Google Workspace. Finally, learners will master the optimization and structuring of resources within their Google Workspace environment.
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Learn to secure your deployments on Google Cloud, including: how to use Cloud Armor bot management to mitigate bot risk and control access from automated clients; use Cloud Armor denylists to restrict or allow access to your HTTP(S) load balancer at the edge of the Google Cloud; apply Cloud Armor security policies to restrict access to cache objects on Cloud CDN and Google Cloud Storage; and mitigate common vulnerabilities using Cloud Armor WAF rules.
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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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In this self-paced training course, participants learn mitigations for attacks at many points in a Google Cloud-based infrastructure, including Distributed Denial-of-Service attacks, phishing attacks, and threats involving content classification and use. They also learn about the Security Command Center, cloud logging and audit logging, and using Forseti to view overall compliance with your organization's security policies.
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This self-paced training course gives participants broad study of security controls and techniques on Google Cloud. Through recorded lectures, demonstrations, and hands-on labs, participants explore and deploy the components of a secure Google Cloud solution, including Cloud Storage access control technologies, Security Keys, Customer-Supplied Encryption Keys, API access controls, scoping, shielded VMs, encryption, and signed URLs. It also covers securing Kubernetes environments.
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This course is intended to give architects, engineers, and developers the skills required to help enterprise customers architect, plan, execute, and test database migration projects. Through a combination of presentations, demos, and hands-on labs participants move databases to Google Cloud while taking advantage of various services.
This course covers how to move on-premises, enterprise databases like SQL Server to Google Cloud (Compute Engine and Cloud SQL) and Oracle to Google Cloud bare metal.
Earn a skill badge by completing the Get Started with Looker skill badge course, where you learn how to analyze, visualize, and curate data using Looker Studio and Looker.
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This self-paced training course gives participants broad study of security controls and techniques on Google Cloud. Through recorded lectures, demonstrations, and hands-on labs, participants explore and deploy the components of a secure Google Cloud solution, including Cloud Identity, Resource Manager, IAM, Virtual Private Cloud firewalls, Cloud Load Balancing, Cloud Peering, Cloud Interconnect, and VPC Service Controls.
This is the first course of the Security in Google Cloud series. After completing this course, enroll in the Security Best Practices in Google Cloud course.
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This course helps learners prepare for the Professional Cloud Security Engineer (PCSE) Certification exam. Learners will be exposed to and engage with exam topics through a series of lectures, diagnostic questions, and knowledge checks. After completing this course, learners will have a personalized workbook that will guide them through the rest of their certification readiness journey.
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This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.
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In this course, you apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine.
This is the fifth and final course of the Advanced Machine Learning on Google Cloud series.
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This course introduces the products and solutions to solve NLP problems on Google Cloud. Additionally, it explores the processes, techniques, and tools to develop an NLP project with neural networks by using Vertex AI and TensorFlow.
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This course describes different types of computer vision use cases and then highlights different machine learning strategies for solving these use cases. The strategies vary from experimenting with pre-built ML models through pre-built ML APIs and AutoML Vision to building custom image classifiers using linear models, deep neural network (DNN) models or convolutional neural network (CNN) models.
The course shows how to improve a model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting the data.
The course also looks at practical issues that arise, for example, when one doesn't have enough data and how to incorporate the latest research findings into different models.
Learners will get hands-on practice building and optimizing their own image classification models on a variety of public datasets in the labs they will work on.
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This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training, and how to write distributed training models with custom estimators.
This is the second course of the Advanced Machine Learning on Google Cloud series. After completing this course, enroll in the Image Understanding with TensorFlow on Google Cloud course.
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This course takes a real-world approach to the ML Workflow through a case study. An ML team faces several ML business requirements and use cases. The team must understand the tools required for data management and governance and consider the best approach for data preprocessing.
The team is presented with three options to build ML models for two use cases. The course explains why they would use AutoML, BigQuery ML, or custom training to achieve their objectives.
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This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to find which data columns make the most useful features. This course also includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.
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This course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML models for scaled use.
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完成使用 BigQuery ML 為預測模型進行資料工程技能徽章中階課程， 即可證明自己具備下列知識與技能：運用 Dataprep by Trifacta 建構連至 BigQuery 的資料轉換 pipeline； 使用 Cloud Storage、Dataflow 和 BigQuery 建構「擷取、轉換及載入」(ETL) 工作負載， 以及使用 BigQuery ML 建構機器學習模型。技能 徽章是 Google Cloud 核發的獨家數位徽章， 用於肯定您在 Google Cloud 產品和服務方面的精熟技能， 代表您已通過測驗，能在互動式實作環境中應用相關知識。完成 這個課程及結業評量挑戰實驗室，即可取得數位徽章 並與他人分享。
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The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code. You will understand the benefits of Big Query ML. We then discuss how to optimize a machine learning (ML) model and how generalization and sampling can help assess the quality of ML models for custom training.
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This course explores what ML is and what problems it can solve. The course also discusses best practices for implementing machine learning. You’re introduced to Vertex AI, a unified platform to quickly build, train, and deploy AutoML machine learning models.
The course discusses the five phases of converting a candidate use case to be driven by machine learning, and why it’s important to not skip them. The course ends with recognizing the biases that ML can amplify and how to recognize them.
完成 在 Google Cloud 為機器學習 API 準備資料 技能徽章入門課程，即可證明您具備下列技能： 使用 Dataprep by Trifacta 清理資料、在 Dataflow 執行資料管道、在 Dataproc 建立叢集和執行 Apache Spark 工作，以及呼叫機器學習 API，包含 Cloud Natural Language API、Google Cloud Speech-to-Text API 和 Video Intelligence API。 「技能徽章」是 Google Cloud 核發的獨家數位徽章，用於肯定您在 Google Cloud 產品與服務方面的精通程度， 代表您已通過測驗，能在互動式實作環境中應用相關知識。完成本技能徽章課程及結業評量挑戰研究室， 即可取得技能徽章並與他人分享。
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In the last installment of the Dataflow course series, we will introduce the components of the Dataflow operational model. We will examine tools and techniques for troubleshooting and optimizing pipeline performance. We will then review testing, deployment, and reliability best practices for Dataflow pipelines. We will conclude with a review of Templates, which makes it easy to scale Dataflow pipelines to organizations with hundreds of users. These lessons will help ensure that your data platform is stable and resilient to unanticipated circumstances.
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In this second installment of the Dataflow course series, we are going to be diving deeper on developing pipelines using the Beam SDK. We start with a review of Apache Beam concepts. Next, we discuss processing streaming data using windows, watermarks and triggers. We then cover options for sources and sinks in your pipelines, schemas to express your structured data, and how to do stateful transformations using State and Timer APIs. We move onto reviewing best practices that help maximize your pipeline performance. Towards the end of the course, we introduce SQL and Dataframes to represent your business logic in Beam and how to iteratively develop pipelines using Beam notebooks.
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This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next, we talk about the Apache Beam vision and the benefits of the Beam Portability framework. The Beam Portability framework achieves the vision that a developer can use their favorite programming language with their preferred execution backend. We then show you how Dataflow allows you to separate compute and storage while saving money, and how identity, access, and management tools interact with your Dataflow pipelines. Lastly, we look at how to implement the right security model for your use case on Dataflow.
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Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.
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This 1-week, accelerated on-demand course builds upon Google Cloud Platform Big Data and Machine Learning Fundamentals. Through a combination of video lectures, demonstrations, and hands-on labs, you'll learn to build streaming data pipelines using Google cloud Pub/Sub and Dataflow to enable real-time decision making. You will also learn how to build dashboards to render tailored output for various stakeholder audiences.
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Data pipelines typically fall under one of the Extract and Load (EL), Extract, Load and Transform (ELT) or Extract, Transform and Load (ETL) paradigms. This course describes which paradigm should be used and when for batch data. Furthermore, this course covers several technologies on Google Cloud for data transformation including BigQuery, executing Spark on Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Dataflow. Learners get hands-on experience building data pipeline components on Google Cloud using Qwiklabs.
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The two key components of any data pipeline are data lakes and warehouses. This course highlights use-cases for each type of storage and dives into the available data lake and warehouse solutions on Google Cloud in technical detail. Also, this course describes the role of a data engineer, the benefits of a successful data pipeline to business operations, and examines why data engineering should be done in a cloud environment.
This is the first course of the Data Engineering on Google Cloud series. After completing this course, enroll in the Building Batch Data Pipelines on Google Cloud course.
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This course helps learners create a study plan for the PDE (Professional Data Engineer) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.
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This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.
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Gmail is Google’s cloud based email service that allows you to access your messages from any computer or device with just a web browser.
In this course, you’ll learn how to compose, send and reply to messages. You will also explore some of the common actions that can be applied to a Gmail message, and learn how to organize your mail using Gmail labels.
You will explore some common Gmail settings and features. For example, you will learn how to manage your own personal contacts and groups, customize your Gmail Inbox to suit your way of working, and create your own email signatures and templates.
Google is famous for search. Gmail also includes powerful search and filtering. You will explore Gmail’s advanced search and learn how to filter messages automatically.
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Good news! There’s a new updated version of this learning path available for you!Open the new Professional Cloud DevOps Engineer Certification Learning Path to begin, once you’ve selected the new path all your current progress will be reflected in the new version.
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Earn a skill badge by completing the Measure Site Reliability using Cloud Operations Suite quest, where you will learn how to set Service Level Indicators (SLIs), Service Level Objectives (SLOs), and Service Level Agreements (SLAs); create logs-based metrics to capture to capture specific issues and address them; define alerts to notify Site Reliability Engineers of issues in production environment, and troubleshoot application issues with Cloud Trace, Debugger, Profiler, Monitoring and Logging. 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 實作 DevOps 工作流程 技能徽章中階課程， 即可證明您具備下列技能：使用 Cloud Source Repositories 建立 Git 存放區、 在 Google Kubernetes Engine (GKE) 發布、管理和調度 Deployment， 以及建立 CI/CD 管道，自動建構容器映像檔與執行 GKE 部署作業。 「技能徽章」是 Google Cloud 核發的獨家數位徽章， 用於肯定您在 Google Cloud 產品與服務方面的精通程度， 代表您已通過測驗，能在互動式實作環境中應用相關知識。完成 本課程及結業評量挑戰研究室，即可取得技能徽章 並與親友分享。
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Course two of the Architecting Hybrid Cloud with Anthos series prepares students to operate and observe Anthos environments. Through presentations and hands-on labs, participants explore adjusting existing clusters, setting up advanced traffic routing policies, securing communication across workloads, and observing clusters in Anthos. This course is a continuation of course one, Multi-Cluster, Multi-Cloud with Anthos, and assumes direct experience with the topics covered in that course.
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This course provides an introduction to using Terraform for Google Cloud. It enables learners to describe how Terraform can be used to implement infrastructure as code and to apply some of its key features and functionalities to create and manage Google Cloud infrastructure.
Learners will get hands-on practice building and managing Google Cloud resources using Terraform.
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In many IT organizations, incentives are not aligned between developers, who strive for agility, and operators, who focus on stability. Site reliability engineering, or SRE, is how Google aligns incentives between development and operations and does mission-critical production support. Adoption of SRE cultural and technical practices can help improve collaboration between the business and IT. This course introduces key practices of Google SRE and the important role IT and business leaders play in the success of SRE organizational adoption.
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Good news! There’s a new updated version of this learning path available for you!Open the new Professional Cloud Network Engineer Certification Learning Path to begin, once you’ve selected the new path all your current progress will be reflected in the new version.
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完成「建構安全的 Google Cloud 網路」課程，即可獲得技能徽章。本課程將說明多項網路相關 資源，協助您在 Google Cloud 建構、調度資源和保護應用程式。
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完成 在 Google Cloud 實作 Cloud 安全防護措施：基礎知識 技能徽章中階課程， 即可證明您具備下列技能：運用 Identity and Access Management (IAM) 建立及指派角色、 建立及管理服務帳戶、啟用虛擬私有雲 (VPC) 網路中的私人連線、 運用 Identity-Aware Proxy 限制應用程式存取權、 運用 Cloud Key Management Service (KMS) 管理金鑰和已加密資料，以及建立私人 Kubernetes 叢集。 「技能徽章」是 Google Cloud 核發的獨家數位徽章， 用於肯定您在 Google Cloud 產品和服務方面的精通程度， 代表您已通過測驗，能在互動式實作環境中應用相關知識。完成 本課程及結業評量挑戰研究室，即可取得技能徽章 並與親友分享。
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This course helps you structure your preparation for the Professional Cloud Engineer exam. You will learn about the Google Cloud domains covered by the exam and how to create a study plan to improve your domain knowledge.
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Networking in Google cloud is a 6 part course series. Welcome to the first course of our six part course series, Networking in Google Cloud: Fundamentals.
This course provides a comprehensive overview of core networking concepts, including networking fundamentals, virtual private clouds (VPCs), and the sharing of VPC networks. Additionally, the course covers network logging and monitoring techniques.
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If you want to take your Google Cloud networking skills to the next level, look no further. This course is composed of labs that cover real-life use cases and it will teach you best practices for overcoming common networking bottlenecks. From getting hands-on practice with testing and improving network performance, to integrating high-throughput VPNs and networking tiers, Network Performance and Optimization is an essential course for Google Cloud developers who are looking to double down on application speed and robustness.
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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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Welcome to the second course in the networking and Google Cloud series routing and addressing.
In this course, we'll cover the central routing and addressing concepts that are relevant to Google Cloud's networking capabilities.
Module one will lay the foundation by exploring network routing and addressing in Google Cloud, covering key building blocks such as routing IPv4, bringing your own IP addresses and setting up cloud DNS. In Module two will shift our focus to private connection options, exploring use cases and methods for accessing Google and other services privately using internal IP addresses.
By the end of this course, you'll have a solid grasp of how to effectively route and address your network traffic within Google Cloud.
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This course helps you structure your preparation for the Associate Cloud Engineer exam. You will learn about the Google Cloud domains covered by the exam and how to create a study plan to improve your domain knowledge.
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Good news! There’s a new updated version of this learning path available for you!Open the new Professional Cloud Architect Certification Learning Path to begin, once you’ve selected the new path all your current progress will be reflected in the new version.
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完成 雲端架構：設計、實作與管理 課程即可獲得 技能徽章，證明您具備下列技能： 使用 Apache 網路伺服器部署可公開存取的網站、使用開機指令碼設定 Compute Engine VM、 使用 Windows 防禦主機和防火牆規則設定安全的 RDP、建構 Docker 映像檔並部署至 Kubernetes 叢集，然後進行更新，以及建立 Cloud SQL 執行個體並匯入 MySQL 資料庫。 這個技能徽章課程是絕佳的 資源，可讓您瞭解Google Cloud 認證專業雲端架構師認證測驗涵蓋的主題。 「技能徽章」是 Google Cloud 核發的獨家數位徽章， 用於肯定您在 Google Cloud 產品與服務方面的精通程度， 代表您已通過測驗，能在互動式實作環境中應用相關知識。完成 本課程及結業評量挑戰研究室，即可獲得技能徽章 並與親友分享。
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完成「設定 Google Cloud 網路」課程，即可獲得技能徽章。 您將瞭解如何在 Google Cloud Platform 執行基本的網路工作，包括建立自訂網路、新增子網路防火牆規則，還有建立 VM 並測試 VM 之間的通訊延遲。 「技能徽章」是 Google Cloud 核發的獨家數位徽章， 用於肯定您在 Google Cloud 產品與服務方面的精熟技能， 並代表您已通過測驗， 能在互動式實作環境中應用相關知識。完成這個課程及結業評量挑戰實驗室， 即可取得數位徽章並與他人分享。
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完成 Google Kubernetes Engine 成本效益最佳化 技能徽章中階課程， 即可證明您具備下列技能：建立及管理多租戶叢集、依據命名空間監控資源使用量、 設定自動調度叢集和 Pod 資源以提升效能、設定負載平衡以最佳化 資源分配，以及導入有效性和完備性探測，確保應用程式維持健康並符合成本效益。 「技能徽章」是 Google Cloud 核發的獨家數位徽章， 用於肯定您在 Google Cloud 產品和服務方面的精熟技能， 代表您已通過測驗，能在互動式實作環境中應用相關知識。完成 本課程及結業評量挑戰實驗室，即可取得技能徽章 並與親友分享。
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完成「在 Google Cloud 使用 Terraform 建構基礎架構」技能徽章中階課程， 即可證明自己具備下列知識與技能：使用 Terraform 的基礎架構即程式碼 (IaC) 原則、運用 Terraform 設定佈建及管理 Google Cloud 資源、有效管理狀態 (本機和遠端)，以及將 Terraform 程式碼模組化，以利重複使用和管理。
只要修完「在 Google Cloud 設定應用程式開發環境」課程，就能獲得技能徽章。 在本課程中，您將學會如何使用以下技術的基本功能，建構和連結以儲存空間為中心的雲端基礎架構：Cloud Storage、Identity and Access Management、Cloud Functions 和 Pub/Sub。 「技能徽章」是 Google Cloud 核發的獨家數位徽章，用於表彰您相當熟悉 Google Cloud 產品與服務，並已通過測驗，能在互動式實作環境中應用相關知識。只要完成這個技能徽章課程和最終評量挑戰研究室，即可取得技能徽章並與親友分享成就。
In "Architecting with Google Kubernetes Engine- Workloads", you'll embark on a comprehensive journey into cloud-native application development. Throughout the learning experience, you'll explore Kubernetes operations, deployment management, GKE networking, and persistent storage. This is the first course of the Architecting with Google Kubernetes Engine series. After completing this course, enroll in the Architecting with Google Kubernetes Engine- Production course.
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In this course, you'll learn about Kubernetes and Google Kubernetes Engine (GKE) security; logging and monitoring; and using Google Cloud managed storage and database services from within GKE.
This is the second course of the Architecting with Google Kubernetes Engine series. After completing this course, enroll in the Reliable Google Cloud Infrastructure: Design and Process course or the Hybrid Cloud Infrastructure Foundations with Anthos course.
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This course helps learners create a study plan for the PCA (Professional Cloud Architect) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.
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在 「Google Kubernetes Engine 架構：基礎知識」的課程中，您將復習 Google Cloud 的配置和原則，接著是建立和管理軟體容器簡介和 Kubernetes 架構簡介。
這是 Google Kubernetes Engine 架構系列中的第一項課程。完成此課程後，請註冊 Google Kubernetes Engine 架構：工作負載課程。
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這堂課程可讓參加人員瞭解如何使用確實有效的設計模式，在 Google Cloud 中打造相當可靠且效率卓越的解決方案。這堂課程接續了「設定 Google Compute Engine 架構」或「設定 Google Kubernetes Engine 架構」課程的內容，並假設參加人員曾實際運用上述任一課程涵蓋的技術。這堂課程結合了簡報、設計活動和實作研究室，可讓參加人員瞭解如何定義業務和技術需求，並在兩者之間取得平衡，設計出相當可靠、可用性高、安全又符合成本效益的 Google Cloud 部署項目。
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This course teaches participants techniques for monitoring and improving infrastructure and application performance in Google Cloud.
Using a combination of presentations, demos, hands-on labs, and real-world case studies, attendees gain experience with full-stack monitoring, real-time log management and analysis, debugging code in production, tracing application performance bottlenecks, and profiling CPU and memory usage.
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這堂隨選密集課程會向參加人員說明 Google Cloud 提供的全方位彈性基礎架構和平台服務，並將重點放在 Compute Engine。這堂課程結合了視訊講座、示範和實作研究室，可讓參加人員探索及部署解決方案元素，例如網路、系統和應用程式服務等基礎架構元件。另外，這堂課也會介紹如何部署實用的解決方案，包括客戶提供的加密金鑰、安全性和存取權管理機制、配額與帳單，以及資源監控功能。
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這堂隨選密集課程會向參加人員說明 Google Cloud 提供的全方位彈性基礎架構和平台服務，尤其側重於 Compute Engine。這堂課程結合了視訊講座、示範和實作研究室，可讓參加人員探索及部署解決方案元素，例如網路、虛擬機器和應用程式服務等基礎架構元件。您會瞭解如何透過控制台和 Cloud Shell 使用 Google Cloud。另外，您也能瞭解雲端架構師的職責、基礎架構設計方法，以及具備虛擬私有雲 (VPC)、專案、網路、子網路、IP 位址、路徑和防火牆規則的虛擬網路設定。
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這堂隨選密集課程會向參加人員說明 Google Cloud 提供的全方位彈性基礎架構和平台服務。這堂課結合了視訊講座、示範和實作研究室，可讓參加人員探索及部署解決方案元素，包括安全地建立互連網路、負載平衡、自動調度資源、基礎架構自動化，以及代管服務。
完成 運用 Cloud Run 開發無伺服器應用程式 技能徽章中階課程， 即可證明您具備下列技能：整合 Cloud Run 和 Cloud Storage 以管理資料、 使用 Cloud Run 和 Pub/Sub 架構可復原的非同步系統、 使用 Cloud Run 建構 REST API 閘道，以及在 Cloud Run 建構及部署服務。 「技能徽章」是 Google Cloud 核發的獨家數位徽章， 用於肯定您在 Google Cloud 產品與服務方面的精通程度， 代表您已通過測驗，能在互動式實作環境中應用相關知識。完成 本課程及結業評量挑戰研究室，即可取得技能徽章 並與親友分享。
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歡迎參加「開始使用 Google Kubernetes Engine」課程。Kubernetes 是位於應用程式和硬體基礎架構之間的軟體層。如果您對這項技術感興趣，這堂課程可以滿足您的需求。有了 Google Kubernetes Engine，您就能在 Google Cloud 中以代管服務的形式使用 Kubernetes。
本課程的目標在於介紹 Google Kubernetes Engine (常簡稱為 GKE) 的基本概念，以及如何將應用程式容器化，以便在 Google Cloud 中執行。課程首先會初步介紹 Google Cloud，隨後簡介容器、Kubernetes、Kubernetes 架構和 Kubernetes 作業。
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In this course, application developers learn how to design and develop cloud-native applications that seamlessly integrate managed services from Google Cloud. Through a combination of presentations, demos, and hands-on labs, participants learn how to apply best practices for application development and use the appropriate Google Cloud storage services for object storage, relational data, caching, and analytics.
Completing one version of each lab is required. Each lab is available in Node.js. In most cases, the same labs are also provided in Python or Java. You may complete each lab in whichever language you prefer.
This is the first course of the Developing Applications with Google Cloud series. After completing this course, enroll in the Securing and Integrating Components of your Application course.
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「Google Cloud 基礎知識：核心基礎架構」介紹了在使用 Google Cloud 時會遇到的重要概念和術語。本課程會透過影片和實作實驗室，介紹並比較 Google Cloud 的多種運算和儲存服務，同時提供重要的資源和政策管理工具。
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Organizations of all sizes are embracing the power and flexibility of the cloud to transform how they operate. However, managing and scaling cloud resources effectively can be a complex task.
Scaling with Google Cloud Operations explores the fundamental concepts of modern operations, reliability, and resilience in the cloud, and how Google Cloud can help support these efforts.
Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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Many traditional enterprises use legacy systems and applications that can't stay up-to-date with modern customer expectations. Business leaders often have to choose between maintaining their aging IT systems or investing in new products and services. "Modernize Infrastructure and Applications with Google Cloud" explores these challenges and offers solutions to overcome them by using cloud technology.
Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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Cloud technology can bring great value to an organization, and combining the power of cloud technology with data has the potential to unlock even more value and create new customer experiences.
“Exploring Data Transformation with Google Cloud” explores the value data can bring to an organization and ways Google Cloud can make data useful and accessible.
Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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There's much excitement about cloud technology and digital transformation, but often many unanswered questions.
For example: What is cloud technology? What does digital transformation mean? How can cloud technology help your organization? Where do you even begin?
If you've asked yourself any of these questions, you're in the right place. This course provides an overview of the types of opportunities and challenges that companies often encounter in their digital transformation journey. If you want to learn about cloud technology so you can excel in your role and help build the future of your business, then this introductory course on digital transformation is for you. This course is part of the Cloud Digital Leader learning path.
Application Development with Cloud Run
Earned Aug 8, 2023 EDT
在 Google Cloud 部署 Kubernetes 應用程式
Earned Aug 8, 2023 EDT
Hybrid Cloud Modernizing Applications with Anthos
Earned Aug 7, 2023 EDT
App Deployment, Debugging, and Performance
Earned Aug 5, 2023 EDT
Securing and Integrating Components of your Application
Earned Aug 5, 2023 EDT
運用 BigQuery ML 建立機器學習模型
Earned Aug 2, 2023 EDT
Create and Manage AlloyDB Instances
Earned Aug 2, 2023 EDT
Migrate MySQL data to Cloud SQL using Database Migration Service
Earned Aug 1, 2023 EDT
Create and Manage Bigtable Instances
Earned Jul 31, 2023 EDT
Google Meet
Earned Jul 27, 2023 EDT
Google Slides
Earned Jul 27, 2023 EDT
Google Docs
Earned Jul 27, 2023 EDT
Google Calendar
Earned Jul 27, 2023 EDT
Google Drive
Earned Jul 27, 2023 EDT
Google Sheets - Advanced Topics
Earned Jul 27, 2023 EDT
Google Sheets
Earned Jul 27, 2023 EDT
Configure your Workplace: Google Workspace for IT Admins
Earned Jul 27, 2023 EDT
DEPRECATED Planning for a Google Workspace Deployment
Earned Jul 26, 2023 EDT
Google Workspace Data Governance
Earned Jul 26, 2023 EDT
Google Workspace Security
Earned Jul 26, 2023 EDT
Google Workspace Core Services
Earned Jul 26, 2023 EDT
Create and Manage Cloud Spanner Instances
Earned Jul 25, 2023 EDT
Google Workspace User and Resource Management
Earned Jul 24, 2023 EDT
Securing your Network with Cloud Armor
Earned Jul 13, 2023 EDT
Google Kubernetes Engine Best Practices: Security
Earned Jul 13, 2023 EDT
Mitigating Security Vulnerabilities on Google Cloud
Earned Jul 12, 2023 EDT
Security Best Practices in Google Cloud
Earned Jul 11, 2023 EDT
Enterprise Database Migration
Earned Jun 30, 2023 EDT
透過 Google Cloud Observability 監控及記錄系統狀態
Earned Jun 20, 2023 EDT
Get Started with Looker
Earned Jun 20, 2023 EDT
Managing Security in Google Cloud
Earned Jun 19, 2023 EDT
Preparing for Your Professional Cloud Security Engineer Journey
Earned Jun 16, 2023 EDT
Machine Learning Operations (MLOps): Getting Started
Earned Jun 8, 2023 EDT
Recommendation Systems on Google Cloud
Earned Jun 8, 2023 EDT
Natural Language Processing on Google Cloud
Earned Jun 8, 2023 EDT
Computer Vision Fundamentals with Google Cloud
Earned Jun 7, 2023 EDT
Production Machine Learning Systems
Earned Jun 5, 2023 EDT
Machine Learning in the Enterprise
Earned May 31, 2023 EDT
Feature Engineering
Earned May 30, 2023 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud
Earned May 29, 2023 EDT
使用 BigQuery ML 為預測模型進行資料工程
Earned May 28, 2023 EDT
Launching into Machine Learning
Earned May 27, 2023 EDT
How Google Does Machine Learning
Earned May 26, 2023 EDT
透過 BigQuery 建構資料倉儲
Earned May 22, 2023 EDT
在 Google Cloud 為機器學習 API 準備資料
Earned May 22, 2023 EDT
Serverless Data Processing with Dataflow: Operations
Earned May 17, 2023 EDT
Serverless Data Processing with Dataflow: Develop Pipelines
Earned May 17, 2023 EDT
Serverless Data Processing with Dataflow: Foundations
Earned May 14, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned May 12, 2023 EDT
Building Resilient Streaming Systems on Google Cloud Platform
Earned May 12, 2023 EDT
Building Batch Data Pipelines on Google Cloud
Earned May 11, 2023 EDT
Modernizing Data Lakes and Data Warehouses with Google Cloud
Earned May 10, 2023 EDT
Preparing for your Professional Data Engineer Journey
Earned May 10, 2023 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned May 8, 2023 EDT
Gmail
Earned Apr 3, 2023 EDT
Certification Learning Path: Professional Cloud DevOps Engineer
Earned Mar 25, 2023 EDT
Measure Site Reliability using Cloud Operations Suite
Earned Mar 25, 2023 EDT
在 Google Cloud 實作 DevOps 工作流程
Earned Mar 19, 2023 EDT
DEPRECATED Cloud Operations and Service Mesh with Anthos
Earned Mar 17, 2023 EDT
Getting Started with Terraform for Google Cloud
Earned Mar 16, 2023 EDT
Developing a Google SRE Culture
Earned Mar 16, 2023 EDT
Certification Learning Path: Professional Cloud Network Engineer
Earned Mar 16, 2023 EDT
建構安全的 Google Cloud 網路
Earned Mar 16, 2023 EDT
在 Google Cloud 實作 Cloud 安全防護措施：基礎知識
Earned Mar 15, 2023 EDT
Preparing for Your Professional Cloud Network Engineer Journey
Earned Mar 15, 2023 EDT
Networking in Google Cloud: Fundamentals
Earned Mar 15, 2023 EDT
DEPRECATED Network Performance and Optimization
Earned Mar 12, 2023 EST
Automate Deployment and Manage Traffic on a Google Cloud Network
Earned Mar 7, 2023 EST
Networking in Google Cloud: Routing and Addressing
Earned Mar 7, 2023 EST
Preparing for Your Associate Cloud Engineer Journey
Earned Feb 28, 2023 EST
Certification Learning Path: Professional Cloud Architect
Earned Feb 27, 2023 EST
雲端架構：設計、實作與管理
Earned Feb 27, 2023 EST
設定 Google Cloud 網路
Earned Feb 27, 2023 EST
Google Kubernetes Engine 成本效益最佳化
Earned Feb 27, 2023 EST
在 Google Cloud 使用 Terraform 建構基礎架構
Earned Feb 26, 2023 EST
建立 Google Cloud 網路
Earned Feb 22, 2023 EST
在 Google Cloud 設定應用程式開發環境
Earned Feb 21, 2023 EST
在 Compute Engine 實作負載平衡功能
Earned Feb 21, 2023 EST
Architecting with Google Kubernetes Engine: Workloads
Earned Feb 20, 2023 EST
Architecting with Google Kubernetes Engine: Production
Earned Feb 20, 2023 EST
Preparing for your Professional Cloud Architect Journey
Earned Feb 15, 2023 EST
Architecting with Google Kubernetes Engine: Foundations - 繁體中文
Earned Feb 14, 2023 EST
可靠的 Google Cloud 基礎架構：設計與程序
Earned Feb 14, 2023 EST
Logging and Monitoring in Google Cloud
Earned Feb 13, 2023 EST
重要的 Google Cloud 基礎架構：核心服務
Earned Feb 13, 2023 EST
重要的 Google Cloud 基礎架構：基本概念
Earned Feb 11, 2023 EST
彈性的 Google Cloud 基礎架構：資源調度與自動化
Earned Feb 10, 2023 EST
使用 Firebase 開發無伺服器應用程式
Earned Feb 1, 2023 EST
運用 Cloud Run 開發無伺服器應用程式
Earned Feb 1, 2023 EST
開始使用 Google Kubernetes Engine
Earned Jan 31, 2023 EST
Getting Started With Application Development
Earned Jan 30, 2023 EST
Google Cloud 基礎知識：核心基礎架構
Earned Jan 23, 2023 EST
Scaling with Google Cloud Operations
Earned Jan 11, 2023 EST
Modernize Infrastructure and Applications with Google Cloud
Earned Jan 11, 2023 EST
Exploring Data Transformation with Google Cloud
Earned Jan 11, 2023 EST
Digital Transformation with Google Cloud
Earned Jan 11, 2023 EST