Getting Started with Terraform for Google Cloud
Earned Jun 16, 2023 EDT
Logging and Monitoring in Google Cloud
Earned Jun 16, 2023 EDT
開始使用 Google Kubernetes Engine
Earned Jun 15, 2023 EDT
彈性的 Google Cloud 基礎架構：資源調度與自動化
Earned Jun 15, 2023 EDT
重要的 Google Cloud 基礎架構：核心服務
Earned Jun 14, 2023 EDT
重要的 Google Cloud 基礎架構：基本概念
Earned Jun 14, 2023 EDT
Google Cloud 基礎知識：核心基礎架構
Earned Jun 13, 2023 EDT
Build a Certification Study Guide: ACE Exam Prep
Earned Jun 12, 2023 EDT
Google Sheets - Advanced Topics
Earned Jun 9, 2023 EDT
Google Meet
Earned Jun 9, 2023 EDT
Google Slides
Earned Jun 9, 2023 EDT
Google Sheets
Earned Jun 9, 2023 EDT
Google Docs
Earned Jun 9, 2023 EDT
Google Drive
Earned Jun 9, 2023 EDT
Google Calendar
Earned Jun 8, 2023 EDT
Gmail
Earned Jun 8, 2023 EDT
Configure your Workplace: Google Workspace for IT Admins
Earned Jun 8, 2023 EDT
實現雲端協作與生產力工作流
Earned Jun 8, 2023 EDT
DEPRECATED Planning for a Google Workspace Deployment
Earned Jun 7, 2023 EDT
Google Workspace 資料治理
Earned Jun 6, 2023 EDT
Google Workspace 安全性
Earned Jun 6, 2023 EDT
Google Workspace 核心服務
Earned Jun 6, 2023 EDT
Google Workspace 的使用者和資源管理功能
Earned Jun 6, 2023 EDT
This workload aims to upskill Google Cloud partners to perform specific tasks for modernization using LookML on BigQuery. A proof-of-concept will take learners through the process of creating LookML visualizations on BigQuery. During this course, learners will be guided specifically on how to write Looker modeling language, also known as LookML and create semantic data models, and learn how LookML constructs SQL queries against BigQuery. At a high level, this course will focus on basic LookML to create and access BigQuery objects, and optimize BigQuery objects with LookML.
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Google Cloud 運算基本概念課程，適合幾乎沒有雲端運算背景或經驗的學員。這些課程會說明雲端運算基本知識、大數據和機器學習的核心概念，以及 Google Cloud 的角色和定位。完成這一系列課程後，學員將能夠闡述這些概念並展示實用技能。學員應依以下順序完成課程：
1. Google Cloud 運算的基本概念：Cloud 運算基礎知識
2. Google Cloud 運算的基本概念：Google Cloud 基礎架構
3. Google Cloud 運算的基本概念：Google Cloud 的網路與安全性
4. Google Cloud 運算的基本概念：Google Cloud 中的資料、機器學習和 AI
第一門課會概略說明雲端運算、Google Cloud 的使用方式，以及不同的運算選項。
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「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。
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完成 從 BigQuery 資料取得深入分析結果 技能徽章入門課程，即可證明您具備下列技能： 撰寫 SQL 查詢、查詢公開資料表、將樣本資料載入 BigQuery、使用 BigQuery 的查詢驗證工具 排解常見語法錯誤，以及在 Data Studio 中 透過連結 BigQuery 資料建立報表。
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In this course, we define what machine learning is and how it can benefit your business. You'll see a few demos of ML in action and learn key ML terms like instances, features, and labels. In the interactive labs, you will practice invoking the pretrained ML APIs available as well as build your own Machine Learning models using just SQL with BigQuery ML.
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The third course in this course series is Achieving Advanced Insights with BigQuery. Here we will build on your growing knowledge of SQL as we dive into advanced functions and how to break apart a complex query into manageable steps.
We will cover the internal architecture of BigQuery (column-based sharded storage) and advanced SQL topics like nested and repeated fields through the use of Arrays and Structs. Lastly we will dive into optimizing your queries for performance and how you can secure your data through authorized views.
After completing this course, enroll in the Applying Machine Learning to your Data with Google Cloud course.
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This is the second course in the Data to Insights course series. Here we will cover how to ingest new external datasets into BigQuery and visualize them with Looker Studio. We will also cover intermediate SQL concepts like multi-table JOINs and UNIONs which will allow you to analyze data across multiple data sources.
Note: Even if you have a background in SQL, there are BigQuery specifics (like handling query cache and table wildcards) that may be new to you.
After completing this course, enroll in the Achieving Advanced Insights with BigQuery course.
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In this course, we see what the common challenges faced by data analysts are and how to solve them with the big data tools on Google Cloud. You’ll pick up some SQL along the way and become very familiar with using BigQuery and Dataprep to analyze and transform your datasets.
This is the first course of the From Data to Insights with Google Cloud series. After completing this course, enroll in the Creating New BigQuery Datasets and Visualizing Insights course.
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This course enables system integrators and partners to understand the principles of automated migrations, plan legacy system migrations to Google Cloud leveraging G4 Platform, and execute a trial code conversion.
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Migration from MySQL to Cloud SQL using Database Migration Service that includes sample mock data and all necessary steps with initial assessment to validation including taking care of migrating users and grants.
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Migration from MySQL to Cloud Spanner using Dataflow that includes sample mock data and all necessary steps with initial assessment to validation including taking care of migrating users and grants.
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This course enables partners to help customers extend their on-premises VMware environments to Google Cloud using Google Cloud VMware Engine. Partners will learn how to architect Google Cloud VMware Engine migration solutions that allow customers to move virtual machines to the cloud with minimal risk. The course also instructs partners on how to solve real-world VMware integration and migration problems.
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(Previously named "Developing apps with Vertex AI Agent Builder: Search". Please note there maybe instances in this course where previous product names and titles are used)
Enterprises of all sizes have trouble making their information readily accessible to employees and customers alike. Internal documentation is frequently scattered across wikis, file shares, and databases. Similarly, consumer-facing sites often offer a vast selection of products, services, and information, but customers are frustrated by ineffective site search and navigation capabilities.
This course teaches you to use AI Applications to integrate enterprise-grade generative AI search.
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This course empowers you to develop scalable, performant LookML (Looker Modeling Language) models that provide your business users with the standardized, ready-to-use data that they need to answer their questions. Upon completing this course, you will be able to start building and maintaining LookML models to curate and manage data in your organization’s Looker instance.
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In this course, you learn how to do the kind of data exploration and analysis in Looker that would formerly be done primarily by SQL developers or analysts. Upon completion of this course, you will be able to leverage Looker's modern analytics platform to find and explore relevant content in your organization’s Looker instance, ask questions of your data, create new metrics as needed, and build and share visualizations and dashboards to facilitate data-driven decision making.
In this quest, you will get hands-on experience with LookML in Looker. You will learn how to write LookML code to create new dimensions and measures, create derived tables and join them to Explores, filter Explores, and define caching policies in LookML.
Earn a skill badge by passing the final quiz, you'll demonstrate your understanding of foundational concepts in generative AI.
A skill badge is a digital badge issued by Google Cloud in recognition of your knowledge of Google Cloud products and services. Share your skill badge by making your profile public and adding it to your social media profile.
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This course explores the Geographic Information Systems (GIS), GIS Visualization, and machine learning enhancements to BigQuery.
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This course explores how to implement a streaming analytics solution using Dataflow and BigQuery.
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This course explores how to leverage Looker to create data experiences and gain insights with modern business intelligence (BI) and reporting.
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This course explores how to implement a streaming analytics solution using Pub/Sub.
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This course continues to explore the implementation of data load and transformation pipelines for a BigQuery Data Warehouse using Dataflow.
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This course continues to explore the implementation of data load and transformation pipelines for a BigQuery Data Warehouse using Cloud Data Fusion.
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This course explores the implementation of data load and transformation pipelines for a BigQuery Data Warehouse using Dataproc.
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This course identifies best practices for migrating data warehouses to BigQuery and the key skills required to perform successful migration.
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透過 DevOps 取得 競爭優勢。DevOps 作業涵蓋組織與文化層面，目標為加速推送軟體、 提高服務穩定性，並為所有軟體相關人員 建立共同擁有權。這堂課程說明如何使用 Google Cloud 提高軟體推送功能的速度、穩定性、可用性和安全性。 開發運作研究與評估計畫已新增至 Google Cloud。想知道自己的團隊表現如何嗎？完成 這個五道選擇題的測驗就能知道答案！
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完成在 Google Cloud 實作 CI/CD 管道技能徽章中階課程，即可獲得技能徽章。 您將透過本課程學習如何使用 Artifact Registry、Cloud Build 和 Cloud Deploy，並且操作 Google Cloud 控制台、Google Cloud CLI、Cloud Run 和 GKE。本課程會介紹如何建構持續整合 管道、儲存及保護構件、掃描安全漏洞、驗證 已核准的發布版本是否有效。此外，您還會實際操作，將應用程式 同時部署至 GKE 和 Cloud Run。
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Google Cloud 的服務在安全上絕不妥協， 因此開發了專用工具，確保所有專案安全無虞， 使用者也能妥善管理身分識別機制。在這堂入門課程中，您會實際使用 Google Cloud 的 Identity and Access Management (IAM) 服務， 練習管理使用者和虛擬機器帳戶。您將 佈建虛擬私有雲和 VPN 來熟悉網路安全功能，並瞭解有哪些工具 可防範資安威脅和資料遺失。
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This course, Architecting with Google Kubernetes Engine: Workloads - Locales, is intended for non-English learners. If you want to take this course in English, please enroll in Architecting with Google Kubernetes Engine: Workloads.
In this course, "Architecting with Google Kubernetes Engine: Workloads," you learn about performing Kubernetes operations; creating and managing deployments; the tools of GKE networking; and how to give your Kubernetes workloads persistent storage.
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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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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This course is the third part of the SAP on Google Cloud Platform learning path. Following the SAP on Google Cloud Foundations eLearning and the SAP on Google Cloud Self-paced labs. Participants should have completed these two components before. This course consists of hands-on labs that provide a holistic experience of optimally configuring SAP on Google Cloud. Participants will learn to configure SAP on Google Cloud, and what best practices are, leaving the course with actionable experience to configure SAP on Google Cloud and run SAP workloads on Google Cloud for their customers.
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This on-demand course equips students to understand, configure, and maintain
multi-cluster Kubernetes infrastructures using Anthos GKE, and Istio-based
service mesh, whether deployed with Anthos on Google Cloud or with Anthos
deployed on VMware.
This is the third, and final, course of the Architecting Hybrid Cloud
Infrastructure with Anthos series.
Completion of the Architecting with Google Kubernetes Engine path is a
prerequisite for this course.
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Welcome to Hybrid Cloud Infrastructure Foundations with Anthos!
This is the first course of the Architecting Hybrid Cloud Infrastructure with Anthos path. Anthos enables you to build and manage modern applications, and gives you the freedom to choose where to run them. Anthos gives you one consistent experience in both your on-premises and cloud environments.
During this course, you will be presented with modules that will take you through skills that you will use as an architect or administrator running Anthos environments. The modules in this course include videos, hands-on labs, and links to helpful documentation.
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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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完成部署與管理 Apigee X技能徽章課程，即可獲得技能徽章。 您將瞭解 Apigee X 架構、如何在 Google Cloud 專案佈建 Apigee X 組織、 如何透過 Apigee API 和 UI 管理 Apigee X，以及如何運用 Cloud Armor 和 Apigee 的威脅防護政策 來保護您的 API。
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Learn how to upgrade capacity for the Apigee for private cloud platform installation, and how to monitor the platform. This is the third and final course of the Installing and Managing Google Cloud's Apigee API Platform for Private Cloud series.
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This course discusses the management and operation of the Apigee platform for private cloud.
It includes topics on operational practices, API deployment, analytics, security and upgrade of the platform. This is the second course of the Installing and Managing Google Cloud's Apigee API Platform for Private Cloud course series. After completing this course, enroll in the On Premises Capacity Upgrade and Monitoring with Google Cloud's Apigee API Platform course.
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This course discusses the upgrade process for Apigee hybrid, and teaches you how to monitor and troubleshoot the hybrid runtime plane components.
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This course discusses how environments are managed in Apigee hybrid, and how runtime plane components are secured. You will also learn how to deploy and debug API proxies in Apigee hybrid, and about capacity planning and scaling.
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This course introduces you to the fundamentals and practices used to install and manage Google Cloud's Apigee API Platform for hybrid cloud.
Through a combination of lectures, a hands-on lab, and supplemental materials, you will learn how to install and operate the Apigee API Platform.
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In this course, you learn how to create APIs that utilize multiple services and how you can use custom code on Apigee. You will also learn about fault handling, and how to share logic between proxies. You learn about traffic management and caching. You also create a developer portal, and publish your API to the portal. You learn about logging and analytics, as well as CI/CD and the different deployment models supported by Apigee.
Through a combination of lectures, hands-on labs, and supplemental materials, you will learn how to design, build, secure, deploy, and manage API solutions using Google Cloud's Apigee API Platform.
This is the third and final course of the Developing APIs with the Apigee API Platform course series.
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In this course, you learn how to secure your APIs. You explore the security concerns you will encounter for your APIs. You learn about OAuth, the primary authorization method for REST APIs. You will learn about JSON Web Tokens (JWTs) and federated security. You also learn about securing against malicious requests, safely sending requests across a public network, and how to secure your data for users of Apigee.
Through a combination of lectures, hands-on labs, and supplemental materials, you will learn how to design, build, secure, deploy, and manage API solutions using Google Cloud's Apigee API Platform.
This is the second course of the Developing APIs with the Apigee API Platform series. After completing this course, enroll in the API Development with the Apigee API Platform course.
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In this course, you learn how to design APIs, and how to use OpenAPI specifications to document them. You learn about the API lifecycle, and how the Apigee API platform helps you manage all aspects of the lifecycle. You learn how APIs can be designed using API proxies, and how APIs are packaged as API products to be used by app developers.
Through a combination of lessons, hands-on labs, and supplemental materials, you will learn how to design, build, secure, deploy, and manage API solutions using Google Cloud's Apigee API Platform. This is the first course of the Developing APIs with the Apigee API Platform series. After completing this course, enroll in the API Security with the Apigee API Platform course.
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The Cloud Foundations Customer Onboarding: Best Practices course enables partners to onboard customers on Google Cloud efficiently and in minimum time, by imparting knowledge, IP, and best practices from the Technical Onboarding Center (TOC) team at Global Delivery Center (GDC). The course explores Cloud Identity and organization, users and groups, administrative access, and resource hierarchy. It also examines network configuration, hybrid connectivity, logging and monitoring, and organizational security.
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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 BigQuery fundamentals for professionals who are familiar with SQL-based cloud data warehouses in Redshift and want to begin working in BigQuery. Through interactive lecture content and hands-on labs, you learn how to provision resources, create and share data assets, ingest data, and optimize query performance in BigQuery. Drawing upon your knowledge of Redshift, you also learn about similarities and differences between Redshift and BigQuery to help you get started with data warehouses in BigQuery. After this course, you can continue your BigQuery journey by completing the skill badge quest titled Build and Optimize Data Warehouses with BigQuery.
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This course aims to upskill Google Cloud partners to perform specific tasks of migrating data from Microsoft SQL Server to CloudSQL using the built-in replication capabilities of SQL Server.
Sample data will be used during the migration. Learners will complete several labs that focus on the process of transferring schema, data, and related processes to corresponding Google Cloud products.
One or more challenge labs will test the learner's understanding of the topics.
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This workload aims to upskill Google Cloud partners to perform specific tasks associated with priority workloads. Learners will perform the tasks of migrating data from five products hosted on Cloudera or Hortonworks to corresponding Google Cloud services and hosted products. The migration solutions addressed will be:
HDFS data to Google Cloud Dataproc and Cloud Storage
Hive data to Cloud Dataproc and the Cloud Dataproc Metastore
Hive data to Google Cloud BigQuery
Impala data to Google Cloud BigQuery
HBase to Google Cloud Bigtable
Sample data will be used during all five migrations. Learners will complete several labs that focus on the process of transferring schema, data and related processes to corresponding Google Cloud products.There will be one or more challenge labs that will test the learners understanding of the topics.
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Welcome to Optimize in BigQuery, where we map Enterprise Data Warehouse concepts and components to BigQuery and Google data services with a focus on optimization.
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Welcome to Design in BigQuery, where we map Enterprise Data Warehouse concepts and components to BigQuery and Google data services with a focus on schema design.
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This course discusses the key elements of Google's Data Warehouse solution portfolio and strategy.
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This is the fourth course of a four-course series for cloud architects and engineers with existing Azure knowledge. It compares Google Cloud and Azure solutions and guides professionals on their use.
This course focuses on deploying and monitoring applications in Google Cloud. The learners apply the knowledge of monitoring and application deployment process in Azure to explore the differences with Google Cloud. Learners get hands-on practice building and managing Google Cloud resources.
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This is the third course of a four-course series for cloud architects and engineers with existing Azure knowledge. It compares Google Cloud and Azure solutions and guides professionals on their use.
This course focuses on storage options and containers in Google Cloud. The learners apply the knowledge of storage and containers in Azure to explore how they differ from Google Cloud. Learners get hands-on practice building and managing Google Cloud resources.
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This is the second course of a four-course series for cloud architects and engineers with existing Azure knowledge. It aims to compare Google Cloud and Azure solutions and guide professionals on their use.
This course focuses on compute resources and load balancing in Google Cloud. The learner will apply the knowledge of using virtual machines and load balancers in Azure to explore the similarities and differences with configuring and managing compute resources and load balancers in Google Cloud. Learners will get hands-on practice building and managing Google Cloud resources.
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This is the first course of a four-course series for cloud architects and engineers with existing Azure knowledge, and it compares Google Cloud and Azure solutions and guides professionals on their use.
This course focuses on Identity and Access Management (IAM) and networking in Google Cloud. The learners apply the knowledge of access management and networking in Azure to explore the similarities and differences with access management and networking in Google Cloud. Learners get hands-on practice building and managing Google Cloud resources.
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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 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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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.
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Earn a skill badge by completing the Create Conversational AI Agents with Dialogflow CX quest, where you will learn how to create a conversational virtual agent, including how to: define intents and entities, use versions and environments, create conversational branching, and use IVR features. 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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Earn a skill badge by completing the Automate Interactions with Contact Center AI quest, where you will learn about the features of Contact Center AI, including how to Build a virtual agent, Design conversation flows for your virtual agent; Add a phone gateway to your virtual agent; Use Dialogflow for troubleshooting; Review logs and debug your virtual agent. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete the skill badge quest, and final assessment challenge lab, to receive a digital badge that you can share with your network.
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Welcome to "CCAI Operations and Implementation", the fourth course in the "Customer Experiences with Contact Center AI" series. In this course, learn some best practices for integrating conversational solutions with your existing contact center software, establishing a framework for human agent assistance, and implementing solutions securely and at scale. In this course, you'll be introduced to Agent Assist and the technology it uses so you can delight your customers with the efficiencies and accuracy of services provided when customers require human agents, connectivity protocols, APIs, and platforms which you can use to create an integration between your virtual agent and the services already established for your business, Dialogflow's Environment Management tool for deployment of different versions of your virtual agent for various purposes, compliance measures and regulations you should be aware of when bringing your virtual agent to production, and you'll be given tips from virtua…
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Welcome to "Virtual Agent Development in Dialogflow CX for Software Devs", the third course in the "Customer Experiences with Contact Center AI" series. In this course, learn how to develop more customized customer conversational solutions using Contact Center Artificial Intelligence (CCAI). In this course, you'll be introduced to more advanced and customized handling for virtual agent conversations that need to look up and convey dynamic data, and methods available to you for testing your virtual agent and logs which can be useful for understanding issues that arise.
This is an intermediate course, intended for learners with the following type of role:
Software developers: Codes computer software in a programming language (e.g., C++, Python, Javascript) and often using an SDK/API.
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Welcome to "Virtual Agent Development in Dialogflow CX for Citizen Devs", the second course in the "Customer Experiences with Contact Center AI" series.
In this course, learn how to develop customer conversational solutions using Contact Center Artificial Intelligence (CCAI). In this course, you'll be introduced to adding voice (telephony) as a communication channel to your virtual agent conversations using Dialogflow CX.
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Welcome to "CCAI Virtual Agent Development in Dialogflow ES for Software Developers", the third course in the "Customer Experiences with Contact Center AI" series.
In this course, learn to use additional features of Dialogflow ES for your virtual agent, create a Firestore instance to store customer data, and implement cloud functions that access the data. With the ability to read and write customer data, learner’s virtual agents are conversationally dynamic and able to defer contact center volume from human agents. You'll be introduced to methods for testing your virtual agent and logs which can be useful for understanding issues that arise. Lastly, learn about connectivity protocols, APIs, and platforms for integrating your virtual agent with services already established for your business.
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Welcome to "Virtual Agent Development in Dialogflow ES for Citizen Devs", the second course in the "Customer Experiences with Contact Center AI" series.
In this course, learn how to develop customer conversational solutions using Contact Center Artificial Intelligence (CCAI). You will use Dialogflow ES to create virtual agents and test them using the Dialogflow ES simulator. This course also provides best practices on developing virtual agents. You will also be introduced to adding voice (telephony) as a communication channel to your virtual agent conversations. Through a combination of presentations, demos, and hands-on labs, participants learn how to create virtual agents.
This is an intermediate course, intended for learners with the following types of roles:
Conversational designers: Designs the user experience of a virtual assistant. Translates the brand's business requirements into natural dialog flows.
Citizen developers: Creates new business applications fo…
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Welcome to "CCAI Conversational Design Fundamentals", the first course in the "Customer Experiences with Contact Center AI" series.
In this course, learn how to design customer conversational solutions using Contact Center Artificial Intelligence (CCAI). You will be introduced to CCAI and its three pillars (Dialogflow, Agent Assist, and Insights), and the concepts behind conversational experiences and how the study of them influences the design of your virtual agent. After taking this course you will be prepared to take your virtual agent design to the next level of intelligent conversation.
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Text Prompt Engineering Techniques introduces you to consider different strategic approaches & techniques to deploy when writing prompts for text-based generative AI tasks.
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This course will help ML Engineers, Developers, and Data Scientists implement Large Language Models for Generative AI use cases with Vertex AI. The first two modules of this course contain links to videos and prerequisite course materials that will build your knowledge foundation in Generative AI. Please do not skip these modules. The advanced modules in this course assume you have completed these earlier modules.
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「探索生成式 AI - Agent Platform」課程包含一系列實驗室， 介紹如何在 Google Cloud 使用生成式 AI。完成這些實驗室之後，您會瞭解如何 在 Agent Platform 使用 Gemini 模型、提示詞設計技巧與最佳做法，以及 AI 在構思想法和文字分類、擷取、摘要等方面的用途。您也會瞭解如何運用 Agent Platform 的自訂訓練功能調整基礎模型， 並部署至 Agent Platform 中的端點。
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本課程會介紹 Vertex AI Studio。您可以運用這項工具和生成式 AI 模型互動、根據商業構想設計原型，並投入到正式環境。透過身歷其境的應用實例、有趣的課程及實作實驗室，您將能探索從提示到正式環境的生命週期，同時學習如何將 Vertex AI Studio 運用在多模態版 Gemini 應用程式、提示設計、提示工程和模型調整。這個課程的目標是讓您能運用 Vertex AI Studio，在專案中發揮生成式 AI 的潛能。
本課程將介紹擴散模型，這是一種機器學習模型，近期在圖像生成領域展現亮眼潛力。概念源自物理學，尤其深受熱力學影響。過去幾年來，在學術界和業界都是炙手可熱的焦點。在 Google Cloud 中，擴散模型是許多先進圖像生成模型和工具的基礎。課程將介紹擴散模型背後的理論，並說明如何在 Vertex AI 上訓練和部署這些模型。
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隨著企業持續擴大使用人工智慧和機器學習，以負責任的方式發展相關技術也日益重要。對許多企業來說，談論負責任的 AI 技術可能不難，如何付諸實行才是真正的挑戰。如要瞭解如何在機構中導入負責任的 AI 技術，本課程絕對能助您一臂之力。
您可以從中瞭解 Google Cloud 目前採取的策略、最佳做法和經驗談，協助貴機構奠定良好基礎，實踐負責任的 AI 技術。
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完成「Introduction to Generative AI」、「Introduction to Large Language Models」和「Introduction to Responsible AI」課程，即可獲得技能徽章。通過最終測驗，就能展現您對生成式 AI 基本概念的掌握程度。
「技能徽章」是 Google Cloud 核發的數位徽章，用於表彰您對 Google Cloud 產品和服務的相關知識。您可以將技能徽章公布在社群媒體的個人資料中，向其他人分享您的成果。
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這個入門微學習課程主要介紹「負責任的 AI 技術」和其重要性，以及 Google 如何在自家產品中導入這項技術。本課程也會說明 Google 的 7 個 AI 開發原則。
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這是一堂入門級的微學習課程，旨在探討大型語言模型 (LLM) 的定義和用途，並說明如何調整提示來提高 LLM 成效。此外，也會介紹多項 Google 工具，協助您自行開發生成式 AI 應用程式。
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這個入門微學習課程主要說明生成式 AI 的定義和使用方式，以及此 AI 與傳統機器學習方法的差異。本課程也會介紹各項 Google 工具，協助您開發自己的生成式 AI 應用程式。
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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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In this intermediate course, you will learn to design, build, and optimize robust batch data pipelines on Google Cloud. Moving beyond fundamental data handling, you will explore large-scale data transformations and efficient workflow orchestration, essential for timely business intelligence and critical reporting.
Get hands-on practice using Dataflow for Apache Beam and Serverless for Apache Spark (Dataproc Serverless) for implementation, and tackle crucial considerations for data quality, monitoring, and alerting to ensure pipeline reliability and operational excellence. A basic knowledge of data warehousing, ETL/ELT, SQL, Python, and Google Cloud concepts is recommended.
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While the traditional approaches of using data lakes and data warehouses can be effective, they have shortcomings, particularly in large enterprise environments. This course introduces the concept of a data lakehouse and the Google Cloud products used to create one. A lakehouse architecture uses open-standard data sources and combines the best features of data lakes and data warehouses, which addresses many of their shortcomings.
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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.
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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In this course you will learn the fundamentals of no-code app development and recognize use cases for no-code apps. The course provides an overview of the AppSheet no-code app development platform and its capabilities. You learn how to create an app with data from spreadsheets, create the app’s user experience using AppSheet views and publish the app to end users.
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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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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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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 Managed Service for Apache Spark 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。
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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.
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。
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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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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
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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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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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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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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。
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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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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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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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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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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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.
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。
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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。
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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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Learn how to use NotebookLM to create a personalized study guide for the Professional Cloud Architect certification exam.
You'll review NotebookLM features, create a notebook in NotebookLM, and learn how to use a study guide to practice for a certification exam.
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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. This course explores the fundamental concepts of modern operations, reliability, and resilience in the cloud, and how Google Cloud can help support these efforts.
As 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 apps that can't stay up-to-date with modern customer expectations. Business leaders often have to choose between maintaining their aging IT systems and investing in new products and services. This course explores these challenges and offers solutions to overcome them by using cloud technology.
As 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 is a powerful asset, and when paired with data, it becomes a catalyst for innovation and enhanced customer experiences. Exploring Data Transformation with Google Cloud examines how organizations can leverage the cloud to make their data more accessible, actionable, and valuable.
As 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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Digital transformation is a critical journey for modern organizations, and establishing a strong baseline in cloud computing is the first step toward driving meaningful innovation. Digital Transformation with Google Cloud introduces the core technologies and strategic frameworks that help organizations modernize their operations. This course explores fundamental cloud concepts, global network infrastructure, and the shared responsibility model to help leaders navigate their path to the cloud with confidence.
As 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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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。
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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Welcome to the two-part course on Logging, Monitoring, and Observability in Google Cloud. The core operations tools in Google Cloud break down into two major categories. The operations-focused components and the application performance management tools. This course, Logging and Monitoring in Google Cloud, covers the operations-focused components including Logging, Monitoring, and Service Monitoring. After taking this course, it is suggested that you complete part 2, Observability in Google Cloud, to learn about the available application performance management tools.
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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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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。
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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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「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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Learn how to use NotebookLM to create a personalized study guide for the Associate Cloud Engineer certification exam.
You'll review NotebookLM features, create a notebook in NotebookLM, and learn how to use a study guide to practice for a certification exam.
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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 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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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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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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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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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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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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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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完成「實現雲端協作與生產力工作流」課程，即可獲得入門級技能徽章。本課程將介紹 Google 的協作平台， 帶您瞭解如何使用 Gmail、日曆、Meet、雲端硬碟、試算表和 AppSheet。
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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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本課程可助您培養在 Google Workspace 環境管理資料的技能。首先，您會瞭解如何在 Gmail 和雲端硬碟，運用資料遺失防護規則防止資料外洩。接著，您將學習使用 Google 保管箱保留、保存及檢索資料。再來，我們會說明如何設定資料地區和匯出方式，以遵守法規要求。最後，您會學習運用標籤區分資料，更安全有條理地管理資料。
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本課程說明如何保護 Google Workspace 環境。學員將導入高強度密碼政策和兩步驟驗證，控管使用者存取權，然後使用安全調查工具，主動找出並應對安全風險。接著，學員將管理第三方應用程式存取權和行動裝置，確保安全無虞。最後，學員將強制執行電子郵件安全性和法規遵循措施，保護機構資料。
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本課程旨在讓學員全面瞭解 Google Workspace 核心服務。學員將瞭解如何啟用、停用及設定這些服務，包括 Gmail、日曆、雲端硬碟、Meet、Chat 和文件。接著，他們將學習如何部署及管理 Gemini，為使用者提供強大助力。最後，學員將瞭解 AppSheet 和 Apps Script 的應用實例，學習如何自動處理工作，並擴充 Google Workspace 應用程式的功能。
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本課程介紹 Google Workspace 的使用者和資源管理功能，學員將瞭解如何根據組織需求來設定組織單位、管理各類型的 Google 群組、培養有關管理 Google Workspace 網域設定的專業知識，最後學會如何最佳化及整理 Google Workspace 環境內的資源。
BI Reporting: Looker Visualization on BigQuery
Earned Oct 19, 2023 EDT
Google Cloud 運算的基本概念：Cloud 運算基礎知識
Earned Aug 7, 2023 EDT
Looker 資料模型管理
Earned Aug 7, 2023 EDT
在 Looker 建構 LookML 物件
Earned Aug 4, 2023 EDT
Data Catalog Fundamentals
Earned Aug 3, 2023 EDT
運用 BigQuery ML 建立機器學習模型
Earned Aug 2, 2023 EDT
從 BigQuery 資料取得深入分析結果
Earned Aug 2, 2023 EDT
Applying Machine Learning to your Data with Google Cloud
Earned Jul 31, 2023 EDT
Achieving Advanced Insights with BigQuery
Earned Jul 30, 2023 EDT
Creating New BigQuery Datasets and Visualizing Insights
Earned Jul 30, 2023 EDT
Exploring and Preparing your Data with BigQuery
Earned Jul 27, 2023 EDT
Modernizing Mainframe Applications with Google Cloud
Earned Jul 26, 2023 EDT
MySQL to Cloud SQL
Earned Jul 26, 2023 EDT
MySQL to Cloud Spanner
Earned Jul 26, 2023 EDT
Deploying and Managing Google Cloud VMware Engine
Earned Jul 26, 2023 EDT
Search with AI Applications
Earned Jul 26, 2023 EDT
Developing Data Models with LookML
Earned Jul 26, 2023 EDT
Analyzing and Visualizing Data in Looker
Earned Jul 26, 2023 EDT
在 Looker 應用進階 LookML 概念
Earned Jul 25, 2023 EDT
Understanding LookML in Looker
Earned Jul 25, 2023 EDT
為 Looker 資訊主頁和報表準備資料
Earned Jul 25, 2023 EDT
Generative AI Fundamentals
Earned Jul 25, 2023 EDT
Data Warehousing for Partners: BigQuery Extended Capabilities
Earned Jul 25, 2023 EDT
Data Warehousing for Partners: Streaming Analytics
Earned Jul 25, 2023 EDT
Data Warehousing for Partners: Analyze Data with Looker
Earned Jul 24, 2023 EDT
Data Warehousing for Partners: Stream Data with Pub/Sub
Earned Jul 24, 2023 EDT
Data Warehousing for Partners: Process Data with Dataflow
Earned Jul 24, 2023 EDT
Data Warehousing for Partners: Cloud Data Fusion Pipelines
Earned Jul 24, 2023 EDT
Data Warehousing for Partners: Process Data with Dataproc
Earned Jul 24, 2023 EDT
Data Warehousing for Partners: Migrate Data to BigQuery
Earned Jul 24, 2023 EDT
DevOps 必備知識
Earned Jul 23, 2023 EDT
在 Google Cloud 實作 CI/CD 管道
Earned Jul 21, 2023 EDT
安全性與身分識別基礎知識
Earned Jul 21, 2023 EDT
Architecting with Google Kubernetes Engine: Workloads - Locales
Earned Jul 21, 2023 EDT
Architecting with Google Kubernetes Engine: Foundations - 繁體中文
Earned Jul 21, 2023 EDT
SAP on Google Cloud
Earned Jul 21, 2023 EDT
Hybrid Cloud Multi-Cluster with Anthos
Earned Jul 20, 2023 EDT
Hybrid Cloud Infrastructure Foundations with Anthos
Earned Jul 20, 2023 EDT
Architecting with Google Kubernetes Engine: Production
Earned Jul 20, 2023 EDT
部署與管理 Apigee X
Earned Jul 20, 2023 EDT
On Premises Capacity Upgrade and Monitoring with Google Cloud's Apigee API Platform
Earned Jul 19, 2023 EDT
On Premises Management, Security, and Upgrade with Google Cloud's Apigee API Platform
Earned Jul 19, 2023 EDT
Upgrading and Monitoring the Apigee Hybrid API Platform
Earned Jul 18, 2023 EDT
Managing and Securing the Apigee Hybrid API Platform
Earned Jul 18, 2023 EDT
Architecting and Installing the Apigee Hybrid API Platform
Earned Jul 17, 2023 EDT
API Development with the Apigee API Platform
Earned Jul 17, 2023 EDT
API Security with the Apigee API Platform
Earned Jul 17, 2023 EDT
API Design and Fundamentals with the Apigee API Platform
Earned Jul 17, 2023 EDT
Cloud Foundations: Customer Onboarding Best Practices
Earned Jul 16, 2023 EDT
Machine Learning Operations (MLOps): Getting Started
Earned Jul 16, 2023 EDT
Recommendation Systems on Google Cloud
Earned Jul 16, 2023 EDT
Natural Language Processing on Google Cloud
Earned Jul 14, 2023 EDT
Computer Vision Fundamentals with Google Cloud
Earned Jul 14, 2023 EDT
BigQuery Fundamentals for Redshift Professionals
Earned Jul 13, 2023 EDT
Microsoft SQL Server to Cloud SQL
Earned Jul 13, 2023 EDT
Cloudera to Google Cloud
Earned Jul 13, 2023 EDT
Data Warehousing for Partners: Optimize in BigQuery
Earned Jul 13, 2023 EDT
Data Warehousing for Partners: Design in BigQuery
Earned Jul 13, 2023 EDT
Data Warehousing for Partners: Enable Google Cloud Customers
Earned Jul 13, 2023 EDT
Deploy and Monitor in Google Cloud for Azure Professionals
Earned Jul 13, 2023 EDT
Google Cloud Storage and Containers for Azure Professionals
Earned Jul 13, 2023 EDT
Google Cloud Compute and Scalability for Azure Professionals
Earned Jul 12, 2023 EDT
Google Cloud IAM and Networking for Azure Professionals
Earned Jul 12, 2023 EDT
Google Kubernetes Engine Best Practices: Security
Earned Jul 11, 2023 EDT
Mitigating Security Vulnerabilities on Google Cloud
Earned Jul 11, 2023 EDT
Security Best Practices in Google Cloud
Earned Jul 11, 2023 EDT
Production Machine Learning Systems
Earned Jul 10, 2023 EDT
Machine Learning in the Enterprise
Earned Jul 10, 2023 EDT
Feature Engineering
Earned Jul 10, 2023 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud
Earned Jul 10, 2023 EDT
Launching into Machine Learning
Earned Jul 10, 2023 EDT
How Google Does Machine Learning
Earned Jul 9, 2023 EDT
DEPRECATED Create Conversational AI Agents with Dialogflow CX
Earned Jul 7, 2023 EDT
Automate Interactions with Contact Center AI
Earned Jul 6, 2023 EDT
CCAI Operations and Implementation
Earned Jul 5, 2023 EDT
Virtual Agent Development in Dialogflow CX for Software Devs
Earned Jul 5, 2023 EDT
Virtual Agent Development in Dialogflow CX for Citizen Devs
Earned Jul 5, 2023 EDT
Virtual Agent Development in Dialogflow ES for Software Devs
Earned Jul 5, 2023 EDT
Virtual Agent Development in Dialogflow ES for Citizen Devs
Earned Jul 5, 2023 EDT
Contact Center AI: Conversational Design Fundamentals
Earned Jul 5, 2023 EDT
Text Prompt Engineering Techniques
Earned Jul 5, 2023 EDT
Implementing Generative AI with Vertex AI
Earned Jul 4, 2023 EDT
探索生成式 AI - Agent Platform
Earned Jul 4, 2023 EDT
Vertex AI Studio 簡介
Earned Jul 4, 2023 EDT
建立圖像說明生成模型
Earned Jul 4, 2023 EDT
Transformer 和 BERT 模型
Earned Jul 4, 2023 EDT
編碼器-解碼器架構
Earned Jul 4, 2023 EDT
注意力機制
Earned Jul 4, 2023 EDT
圖像生成簡介
Earned Jul 4, 2023 EDT
負責任的 AI 技術：透過 Google Cloud 採用 AI 開發原則
Earned Jul 4, 2023 EDT
Generative AI Fundamentals - 繁體中文
Earned Jul 4, 2023 EDT
負責任的 AI 技術簡介
Earned Jul 4, 2023 EDT
大型語言模型簡介
Earned Jul 4, 2023 EDT
生成式 AI 簡介
Earned Jul 4, 2023 EDT
Serverless Data Processing with Dataflow: Foundations
Earned Jul 4, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Jul 4, 2023 EDT
Building Resilient Streaming Systems on Google Cloud Platform
Earned Jul 4, 2023 EDT
Build Batch Data Pipelines on Google Cloud
Earned Jul 3, 2023 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned Jul 3, 2023 EDT
Preparing for your Professional Data Engineer Journey
Earned Jul 3, 2023 EDT
透過 Google Cloud Observability 監控及記錄系統狀態
Earned Jul 3, 2023 EDT
在 Google Cloud 實作 DevOps 工作流程
Earned Jul 2, 2023 EDT
DEPRECATED Cloud Operations and Service Mesh with Anthos
Earned Jul 2, 2023 EDT
Building No-Code Apps with AppSheet: Foundations
Earned Jun 30, 2023 EDT
[DEPRECATED] Google Cloud Big Data and Machine Learning Fundamentals
Earned Jun 30, 2023 EDT
Managing Security in Google Cloud
Earned Jun 28, 2023 EDT
Preparing for Your Professional Cloud Security Engineer Journey
Earned Jun 28, 2023 EDT
在 Google Cloud 為機器學習 API 準備資料
Earned Jun 27, 2023 EDT
Developing a Google SRE Culture
Earned Jun 27, 2023 EDT
在 Google Cloud 部署 Kubernetes 應用程式
Earned Jun 27, 2023 EDT
使用 Firebase 開發無伺服器應用程式
Earned Jun 27, 2023 EDT
運用 Cloud Run 開發無伺服器應用程式
Earned Jun 26, 2023 EDT
Hybrid Cloud Modernizing Applications with Anthos
Earned Jun 26, 2023 EDT
Application Development with Cloud Run
Earned Jun 26, 2023 EDT
App Deployment, Debugging, and Performance
Earned Jun 26, 2023 EDT
Securing and Integrating Components of your Application
Earned Jun 26, 2023 EDT
Getting Started With Application Development
Earned Jun 25, 2023 EDT
Architecting with Google Kubernetes Engine: Workloads
Earned Jun 23, 2023 EDT
Architecting with Google Kubernetes Engine: Foundations - 繁體中文
Earned Jun 23, 2023 EDT
在 Google Cloud 實作 Cloud 安全防護措施：基礎知識
Earned Jun 23, 2023 EDT
DEPRECATED Network Performance and Optimization
Earned Jun 22, 2023 EDT
建構安全的 Google Cloud 網路
Earned Jun 22, 2023 EDT
Networking in Google Cloud: Routing and Addressing
Earned Jun 22, 2023 EDT
Networking in Google Cloud: Fundamentals
Earned Jun 22, 2023 EDT
Automate Deployment and Manage Traffic on a Google Cloud Network
Earned Jun 22, 2023 EDT
Preparing for Your Professional Cloud Network Engineer Journey
Earned Jun 21, 2023 EDT
雲端架構：設計、實作與管理
Earned Jun 21, 2023 EDT
Google Kubernetes Engine 成本效益最佳化
Earned Jun 20, 2023 EDT
設定 Google Cloud 網路
Earned Jun 19, 2023 EDT
可靠的 Google Cloud 基礎架構：設計與程序
Earned Jun 19, 2023 EDT
Build a Certification Study Guide: PCA Exam Prep
Earned Jun 19, 2023 EDT
Scaling with Google Cloud Operations
Earned Jun 18, 2023 EDT
Modernize Infrastructure and Applications with Google Cloud
Earned Jun 18, 2023 EDT
Exploring Data Transformation with Google Cloud
Earned Jun 18, 2023 EDT
Digital Transformation with Google Cloud
Earned Jun 18, 2023 EDT
在 Google Cloud 使用 Terraform 建構基礎架構
Earned Jun 17, 2023 EDT
建立 Google Cloud 網路
Earned Jun 17, 2023 EDT
在 Google Cloud 設定應用程式開發環境
Earned Jun 16, 2023 EDT
在 Compute Engine 導入 Cloud Load Balancing
Earned Jun 16, 2023 EDT
Getting Started with Terraform for Google Cloud
Earned Jun 16, 2023 EDT
Logging and Monitoring in Google Cloud
Earned Jun 16, 2023 EDT
開始使用 Google Kubernetes Engine
Earned Jun 15, 2023 EDT
彈性的 Google Cloud 基礎架構：資源調度與自動化
Earned Jun 15, 2023 EDT
重要的 Google Cloud 基礎架構：核心服務
Earned Jun 14, 2023 EDT
重要的 Google Cloud 基礎架構：基本概念
Earned Jun 14, 2023 EDT
Google Cloud 基礎知識：核心基礎架構
Earned Jun 13, 2023 EDT
Build a Certification Study Guide: ACE Exam Prep
Earned Jun 12, 2023 EDT
Google Sheets - Advanced Topics
Earned Jun 9, 2023 EDT
Google Meet
Earned Jun 9, 2023 EDT
Google Slides
Earned Jun 9, 2023 EDT
Google Sheets
Earned Jun 9, 2023 EDT
Google Docs
Earned Jun 9, 2023 EDT
Google Drive
Earned Jun 9, 2023 EDT
Google Calendar
Earned Jun 8, 2023 EDT
Gmail
Earned Jun 8, 2023 EDT
Configure your Workplace: Google Workspace for IT Admins
Earned Jun 8, 2023 EDT
實現雲端協作與生產力工作流
Earned Jun 8, 2023 EDT
DEPRECATED Planning for a Google Workspace Deployment
Earned Jun 7, 2023 EDT
Google Workspace 資料治理
Earned Jun 6, 2023 EDT
Google Workspace 安全性
Earned Jun 6, 2023 EDT
Google Workspace 核心服務
Earned Jun 6, 2023 EDT
Google Workspace 的使用者和資源管理功能
Earned Jun 6, 2023 EDT