Getting Started with Go on Google Cloud
Earned Mar 16, 2021 EDT
DEPRECATED Websites and Web Applications
Earned Mar 14, 2021 EDT
Build Apps & Websites with Firebase
Earned Mar 13, 2021 EST
DEPRECATED Applied Data: Blockchain
Earned Aug 30, 2020 EDT
DEPRECATED BigQuery Basics for Data Analysts
Earned Aug 30, 2020 EDT
DEPRECATED BigQuery for Marketing Analysts
Earned Aug 30, 2020 EDT
Baseline: Deploy & Develop
Earned Aug 29, 2020 EDT
Google Workspace Essentials
Earned Aug 22, 2020 EDT
Google Developer Essentials
Earned Aug 22, 2020 EDT
[DEPRECATED] OK Google: Build Interactive Apps with Google Assistant
Earned Aug 22, 2020 EDT
使用 BigQuery 進行機器學習
Earned Aug 19, 2020 EDT
Advanced ML: ML Infrastructure
Earned Aug 19, 2020 EDT
DEPRECATED Google Cloud Solutions II: Data and Machine Learning
Earned Aug 16, 2020 EDT
[DEPRECATED] Data Engineering
Earned Aug 16, 2020 EDT
Machine Learning APIs
Earned Aug 15, 2020 EDT
Intro to ML: Image Processing
Earned Aug 15, 2020 EDT
機器學習簡介：語言處理
Earned Aug 13, 2020 EDT
Intermediate ML: TensorFlow on Google Cloud
Earned Aug 11, 2020 EDT
Google Cloud Essentials
Earned Aug 9, 2020 EDT
基本概念：資料、機器學習和 AI
Earned Aug 9, 2020 EDT
基本概念：基礎架構
Earned Aug 9, 2020 EDT
DEPRECATED BigQuery for Data Warehousing
Earned Aug 8, 2020 EDT
本課程涵蓋「AI 隱私權」和「AI 安全性」這兩個重要主題。我們將介紹實用的方法和工具，協助您運用 Google Cloud 產品和開放原始碼工具，導入 AI 隱私權和安全性的建議做法。
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This on-demand course provides partners the skills required to design, deploy, and monitor Vertail AI Search for Commerce solutions including retail search and recommendation AI for enterprise customers.
This course demonstrates the power of integrating Google Cloud services and tools with Workspace applications - like using Node.js to build a survey bot, the Natural Language API to recognize sentiment in a Google Doc, and building a chat bot with Apps Script.
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Earn a skill badge by completing the Build Custom Processors with Document AI course. You learn how to extract data and classify documents by creating custom ML models specific to your business needs. This course teaches the foundation skills of building your own processors, working with optical character recognition, form parsing, processor creation, and uptraining the DocumentAI model.
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完成「開始使用 Sensitive Data Protection」 技能徽章入門課程，證明您具備下列技能：使用 Sensitive Data Protection 服務 (包括 Cloud Data Loss Prevention API) 來檢查、遮蓋及去識別化 Google Cloud 中的機密資料。
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Complete the intermediate Secure Software Delivery skill badge to demonstrate your proficiency in proactively integrating security into the software development lifecycle (SDLC) with DevSecOps principles. You'll learn how to utilize Google Kubernetes Engine (GKE) and Cloud Run for secure container image deployment, implement automated vulnerability scanning to proactively identify risks, and streamline application development with Artifact Registry while maintaining a focus on security. Additionally, you'll gain skills in integrating Cloud Build for robust development processes and implementing Admission Control Policies for fine-grained control over your environment.
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The Google Cloud Computing Foundations courses are for individuals with little to no background or experience in cloud computing. They provide an overview of concepts central to cloud basics, big data, and machine learning, and where and how Google Cloud fits in.
By the end of the series of courses, learners will be able to articulate these concepts and demonstrate some hands-on skills.
The courses should be completed in the following order:
1. Google Cloud Computing Foundations: Cloud Computing Fundamentals
2. Google Cloud Computing Foundations: Infrastructure in Google Cloud
3. Google Cloud Computing Foundations: Networking and Security in Google Cloud
4. Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
This final course in the series reviews managed big data services, machine learning and its value, and how to demonstrate your skill set in Google Cloud further by earning Skill Badges.
完成「在 Vertex AI 使用 Gemini API 探索生成式 AI」技能徽章中階課程，即可證明自己具備下列技能： 可運用 Gemini API 生成文字、分析圖片和影片來強化內容創作能力，還能使用函式呼叫技巧。 本課程將帶您瞭解如何善用進階的 Gemini 技術、使用多模態內容生成功能，並提升 AI 專案的潛力。
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本課程會說明 Gemini in BigQuery，這是一套由 AI 輔助的功能，可協助「從資料到 AI」的工作流程。這些功能包含資料探索和準備、程式碼生成和疑難排解，以及工作流程探索和視覺化。本課程將透過概念解說、應用實例和實作實驗室，協助資料從業人員提升工作效率，並加速開發 pipeline。
完成「在 Google Cloud 使用機器學習 API」課程，即可獲得進階技能徽章。本課程說明以下機器學習和 AI 技術的基本功能： Cloud Vision API、Cloud Translation API 和 Cloud Natural Language API。
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完成使用 Document AI 大規模自動擷取資料課程，即可獲得入門級技能徽章。在本課程中，您將瞭解如何使用 Document AI 擷取、處理及提取資料。
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完成「Cloud Speech API：3 種應用」課程，瞭解如何使用語音相關 API 工具合成及轉錄語音， 即可獲得入門級技能徽章。
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完成「Gemini 和 Imagen 實務應用：建構 AI 應用程式」技能徽章入門課程，即可證明您具備下列技能：圖片辨識、自然語言處理、
使用 Google 強大的 Gemini 和 Imagen 模型生成圖片，以及在 Vertex AI 平台上部署應用程式。
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完成 在 Vertex AI 設計提示 技能徽章入門課程，即可證明您具備下列技能： 在 Vertex AI 設計提示、分析圖片，以及運用多模態模型生成內容。瞭解如何建立有效的提示、引導生成式 AI 輸出內容， 以及將 Gemini 模型用於實際的行銷情境。
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探索生成式 AI - Vertex AI 課程包含一系列實驗室，幫助您瞭解 如何在 Google Cloud 使用生成式 AI。透過實驗室，您將瞭解 如何使用 Vertex AI PaLM API 系列模型，包括 text-bison、chat-bison、 和 textembedding-gecko。您也會瞭解提示設計、最佳做法、 以及這些模型如何用於構思、文字分類、文字擷取、文字 摘要等。您也會瞭解如何透過 Vertex AI 自訂訓練功能調整基礎模型， 並將模型部署至 Vertex AI 端點。
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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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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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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。
完成 透過 Vertex AI 建構及部署機器學習解決方案 課程，即可瞭解如何使用 Google Cloud 的 Vertex AI 平台、AutoML 和自訂訓練服務， 訓練、評估、調整、解釋及部署機器學習模型。 這個技能徽章課程適合專業數據資料學家和機器學習 工程師，完成即可取得中階技能徽章。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應用相關知識。完成這個技能徽章課程 和結業評量挑戰實驗室，就能獲得數位徽章， 並與親友分享。
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Welcome to the Learn To Earn Cloud Challenge data track! These eight labs give you a deep dive into GCP's data universe. At the end of each lab, you'll have another in-demand skill to add to your list. Complete this game to earn the Data game badge, and you'll be one step closer to collecting all four Learn to Earn Cloud Challenge badges (see "what's next" below for more information). Race the clock to increase your score and watch your name rise on the leaderboard. Good luck!
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Welcome to the Learn To Earn Cloud Challenge! These eight labs give you a quick hands-on introduction to eight different GCP tools and services. At the end of each lab, you'll have another skill to add to your list. Complete this game to earn the Essentials game badge, and you'll be one step closer to collecting all four Learn to Earn Cloud Challenge badges (see "what's next" below for more information). Race the clock to increase your score and watch your name rise on the leaderboard. Good luck!
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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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This intermediate-level quest is unique among Qwiklabs quests. These labs have been curated to give operators hands-on practice with Anthos—a new, open application modernization platform on GCP. Anthos enables you to build and manage modern hybrid applications. Tasks include: installing service mesh, collecting telemetry, and securing your microservices with service mesh policies. This quest is composed of labs targeted to teach you everything you need to know to introduce service mesh, and Anthos, into your next hybrid cloud project.
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In this advanced-level quest, you will learn the ins and outs of developing GCP applications in Python. The first labs will walk you through the basics of environment setup and application data storage with Cloud Datastore. Once you have a handle on the fundamentals, you will get hands-on practice deploying Python applications on Kubernetes and App Engine (the latter is the same framework that powers Snapchat!) With specialized bonus labs that teach user authentication and backend service development, this quest will give you practical experience so you can start developing robust Python applications straight away.
Learn the ins and outs of Google Cloud's operations suite, an important service for generating insights into the health of your applications. It provides a wealth of information in application monitoring, report logging, and diagnoses. These labs will give you hands-on practice with and will teach you how to monitor virtual machines, generate logs and alerts, and create custom metrics for application data. It is recommended that the students have at least earned a Badge by completing the Google Cloud Essentials. Looking for a hands on challenge lab to demonstrate your skills and validate your knowledge? On completing this course, enroll in and finish the challenge lab at the end of the Monitor and Log with Google Cloud Operations Suite to receive an exclusive Google Cloud digital badge.
Earn a skill badge by completing the Build Interactive Apps with Google Assistant quest, where you will learn how to build Google Assistant applications, including how to: create an Actions project, integrate Dialogflow with an Actions project, test your application with Actions simulator, build an Assistant application with flash cards template, integrate customer MP3 files with your Assistant application, add Cloud Translation API to your Assistant application, and use APIs and integrate them into your applications. 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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Kubernetes 是最受歡迎的容器自動化調度管理系統，Google Kubernetes Engine 則專門支援 Google Cloud 中的 代管 Kubernetes 部署項目。這門進階課程將帶您實際練習設定 Docker 映像檔和容器，並部署完整的 Kubernetes Engine 應用程式。 您會學到如何將容器自動化調度管理機制， 整合到自己的工作流程，這些技巧相當實用。 想透過實作挑戰實驗室展現 技能、驗收學習成果嗎？本課程結束後，再完成 在 Google Cloud 部署 Kubernetes 應用程式課程 結尾的挑戰實驗室，即可獲得專屬 Google Cloud 數位徽章。
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Google Cloud Application Programming Interfaces are the mechanism to interact with Google Cloud Services programmatically. This quest will give you hands-on practice with a variety of GCP APIs, which you will learn through working with Google’s APIs Explorer, a tool that allows you to browse APIs and run their methods interactively. By learning how to transfer data between Cloud Storage buckets, deploy Compute Engine instances, configure Dataproc clusters and much more, Exploring APIs will show you how powerful APIs are and why they are used almost exclusively by proficient GCP users. Enroll in this quest today.
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Google Cloud is committed to supporting Windows workloads in its frameworks and services. In this quest, you will get hands-on practice running Microsoft’s ASP.net (web app framework) on Google Cloud. ASP.NET is an open-source and cross-platform framework for building modern cloud-based and internet-connected applications using the C# programming language.
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。
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完成透過 Google Cloud 建構網站技能徽章課程，即可獲得入門級技能徽章。 本課程以 Get Cooking in Cloud 系列影片為基礎， 涵蓋以下主題：在 Cloud Run 部署網站在 Compute Engine 託管網頁應用程式在 Google Kubernetes Engine 建立、 部署及擴充網站使用 Cloud Build 將單體式應用程式遷移至微服務架構
Get started with Go (Golang) by reviewing Go code, and then creating and deploying simple Go apps on Google Cloud. Go is an open source programming language that makes it easy to build fast, reliable, and efficient software at scale. Go runs native on Google Cloud, and is fully supported on Google Kubernetes Engine, Compute Engine, App Engine, Cloud Run, and Cloud Functions. Go is a compiled language and is faster and more efficient than interpreted languages. As a result, Go requires no installed runtime like Node, Python, or JDK to execute.
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When it comes to hosting websites and web applications, you want a framework that’s robust, fast, and secure. By choosing the Google Cloud Platform, you will have all of those needs covered. In this fundamental-level quest, you will get hands-on practice with GCPs key infrastructure and computing services for the web. From deploying your first web app, to integrating Cloud SQL with Ruby on Rails, to mapping the NYC subway system on App Engine, you will learn all the skills needed to harness GCPs web hosting power.
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Firebase is a backend-as-service (Bass) platform for creating mobile and web applications. In this quest you will learn to build serverless web apps, import data into a serverless database, and build a Google Assistant application with Firebase and its Google Cloud integrations. Looking for a hands on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, enroll in and finish the additional challenge lab at the end of this quest to receive an exclusive Google Cloud digital badge.
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Blockchain and related technologies, such as distributed ledger and distributed apps, are becoming new value drivers and solution priorities in many industries. In this course you will gain hands-on experience with distributed ledger and the exploration of blockchain datasets in Google Cloud. It brings the research and solution work of Google's Allen Day into self-paced labs for you to run and learn directly. Since this course uses advanced SQL in BigQuery, a SQL-in-BigQuery refresher lab is at the start.
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Want to scale your data analysis efforts without managing database hardware? Learn the best practices for querying and getting insights from your data warehouse with this interactive series of BigQuery labs. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights.
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Want to turn your marketing data into insights and build dashboards? Bring all of your data into one place for large-scale analysis and model building. Get repeatable, scalable, and valuable insights into your data by learning how to query it and using BigQuery. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights.
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In this introductory-level quest, you will learn the fundamentals of developing and deploying applications on the Google Cloud Platform. You will get hands-on experience with the Google App Engine framework by launching applications written in languages like Python, Ruby, and Java (just to name a few). You will see first-hand how straightforward and powerful GCP application frameworks are, and how easily they integrate with GCP database, data-loss prevention, and security services.
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Workspace is Google's collaborative applications platform, delivered from Google Cloud. In this introductory-level course you will get hands-on practice with Workspace’s core applications from a user perspective. Although there are many more applications and tool components to Workspace than are covered here, you will get experience with the primary apps: Gmail, Calendar, Sheets and a handful of others. Each lab can be completed in 10-15 minutes, but extra time is provided to allow self-directed free exploration of the applications.
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This introductory-level quest shows application developers how the Google Cloud ecosystem could help them build secure, scalable, and intelligent cloud native applications. You learn how to develop and scale applications without setting up infrastructure, run data analytics, gain insights from data, and develop with pre-trained ML APIs to leverage machine learning even if you are not a Machine Learning expert. You will also experience seamless integration between various Google services and APIs to create intelligent apps.
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With Google Assistant part of over a billion consumer devices, this quest teaches you how to build practical Google Assistant applications integrated with Google Cloud services via APIs. Example apps will use the Dialogflow conversational suite and the Actions and Cloud Functions frameworks. You will build 5 different applications that explore useful and fun tools you can extend on your own. No hardware required! These labs use the cloud-based Google Assistant simulator environment for developing and testing, but if you do have your own device, such as a Google Home or a Google Hub, additional instructions are provided on how to deploy your apps to your own hardware.
Machine Learning is one of the most innovative fields in technology, and the Google Cloud Platform has been instrumental in furthering its development. With a host of APIs, Google Cloud has a tool for just about any machine learning job. In this advanced-level course, you will get hands-on practice with machine learning at scale and how to employ the advanced ML infrastructure available on Google Cloud.
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In this advanced-level quest, you will learn how to harness serious Google Cloud computing power to run big data and machine learning jobs. The hands-on labs will give you use cases, and you will be tasked with implementing big data and machine learning practices utilized by Google’s very own Solutions Architecture team. From running Big Query analytics on tens of thousands of basketball games, to training TensorFlow image classifiers, you will quickly see why Google Cloud is the go-to platform for running big data and machine learning jobs.
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This advanced-level quest is unique amongst the other catalog offerings. The labs have been curated to give IT professionals hands-on practice with topics and services that appear in the Google Cloud Certified Professional Data Engineer Certification. From Big Query, to Dataprep, to Cloud Composer, this quest is composed of specific labs that will put your Google Cloud data engineering knowledge to the test. Be aware that while practice with these labs will increase your skills and abilities, you will need other preparation, too. The exam is quite challenging and external studying, experience, and/or background in cloud data engineering is recommended. Looking for a hands on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, enroll in and finish the additional challenge lab at the end of the Engineer Data in the Google Cloud to receive an exclusive Google Cloud digital badge.
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It's no secret that machine learning is one of the fastest growing fields in tech, and Google Cloud has been instrumental in furthering its development. With a host of APIs, Google Cloud has a tool for just about any machine learning job. In this advanced-level course, you will get hands-on practice with machine learning APIs by taking labs like Detect Labels, Faces, and Landmarks in Images with the Cloud Vision API. Looking for a hands-on challenge lab to demonstrate your skills and validate your knowledge? Enroll in and finish the additional challenge lab at the end of this quest to receive an exclusive Google Cloud digital badge.
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Using large scale computing power to recognize patterns and "read" images is one of the foundational technologies in AI, from self-driving cars to facial recognition. The Google Cloud Platform provides world class speed and accuracy via systems that can utilized by simply calling APIs. With these and a host of other APIs, GCP has a tool for just about any machine learning job. In this introductory quest, you will get hands-on practice with machine learning as it applies to image processing by taking labs that will enable you to label images, detect faces and landmarks, as well as extract, analyze, and translate text from within images.
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大家都知道，機器學習是發展最快的科技領域之一， 而 Google Cloud Platform 在這方面功不可沒。 GCP 提供多種 API，凡是與機器學習相關的任務，幾乎都能處理。您將在本入門課程的 實驗室，實際演練機器學習技術 在語言處理方面的應用，學會如何從文中擷取實體資訊、 執行情緒和語法分析，並使用 Speech-to-Text API 轉錄語音。
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TensorFlow is an open source software library for high performance numerical computation that's great for writing models that can train and run on platforms ranging from your laptop to a fleet of servers in the Cloud to an edge device. This quest takes you beyond the basics of using predefined models and teaches you how to build, train and deploy your own on Google Cloud.
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In this introductory-level course, you get hands-on practice with the Google Cloud’s fundamental tools and services. Optional videos are provided to provide more context and review for the concepts covered in the labs. Google Cloud Essentials is a recommendeded first course for the Google Cloud learner - you can come in with little or no prior cloud knowledge, and come out with practical experience that you can apply to your first Google Cloud project. From writing Cloud Shell commands and deploying your first virtual machine, to running applications on Kubernetes Engine or with load balancing, Google Cloud Essentials is a prime introduction to the platform’s basic features.
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大數據、機器學習和人工智慧 (AI) 是時下熱門的 電腦相關話題，但這些領域相當專業，就算想要入門 也難以取得教材或資料。幸好，Google Cloud 提供了此領域的多種服務，而且容易使用。 參加這堂入門課程，您就能踏出第一步， 開始學習運用 BigQuery、Cloud Speech API 以及 Video Intelligence 等工具。
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如果您是剛起步的雲端開發人員， 想在 Google Cloud Essentials 外獲得更多實作經驗，歡迎參加本課程。您將透過實作實驗室， 深入瞭解 Cloud Storage 和其他重要應用程式服務，例如： Monitoring 和 Cloud Functions。您將習得 在任何 Google Cloud 專案都適用的寶貴技能。
了解详情
Looking to build or optimize your data warehouse? Learn best practices to Extract, Transform, and Load your data into Google Cloud with BigQuery. In this series of interactive labs you will create and optimize your own data warehouse using a variety of large-scale BigQuery public datasets. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights. Looking for a hands on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, enroll in and finish the additional challenge lab at the end of this quest to receive an exclusive Google Cloud digital badge.
開發人員的負責任 AI 技術：隱私權與安全性
Earned Oct 5, 2025 EDT
Vertex AI Search for Commerce
Earned Oct 5, 2025 EDT
在 BigQuery 使用 Gemini 模型
Earned Sep 29, 2025 EDT
Workspace: Add-ons
Earned Sep 29, 2025 EDT
Build Custom Processors with Document AI
Earned Sep 29, 2025 EDT
開始使用 Sensitive Data Protection
Earned Sep 29, 2025 EDT
Secure Software Delivery
Earned Sep 29, 2025 EDT
Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
Earned Sep 28, 2025 EDT
運用 BigQuery 導入多模態向量搜尋
Earned Sep 22, 2025 EDT
使用 Gemini：數據資料學家和分析師
Earned Sep 22, 2025 EDT
在 Google Cloud 為機器學習 API 準備資料
Earned Sep 22, 2025 EDT
使用 Gemini 和 Streamlit 開發生成式 AI 應用程式
Earned Sep 22, 2025 EDT
使用 Gemini 多模態功能和多模態 RAG 檢查複合型文件
Earned Sep 22, 2025 EDT
在 Vertex AI 使用 Gemini API 探索生成式 AI
Earned Sep 22, 2025 EDT
透過 Gemini in BigQuery 提升工作效率
Earned Sep 20, 2025 EDT
運用 Gemini 分析多模態資料並推論
Earned Sep 20, 2025 EDT
強化 Gemini 模型功能
Earned Sep 19, 2025 EDT
保護 BigLake 資料
Earned Sep 19, 2025 EDT
Google Cloud Compute 基本操作
Earned Sep 19, 2025 EDT
在 Google Cloud 使用機器學習 API
Earned Sep 19, 2025 EDT
使用 Document AI 大規模自動擷取資料
Earned Sep 19, 2025 EDT
Cloud Speech API：3 種應用
Earned Sep 18, 2025 EDT
Gemini 和 Imagen 實務應用：建構 AI 應用程式
Earned Sep 18, 2025 EDT
在 Vertex AI 設計提示
Earned Sep 18, 2025 EDT
探索生成式 AI - Vertex AI
Earned Mar 29, 2024 EDT
負責任的 AI 技術：透過 Google Cloud 採用 AI 開發原則
Earned Feb 7, 2024 EST
Generative AI Fundamentals - 繁體中文
Earned Feb 6, 2024 EST
Vertex AI Studio 簡介
Earned Oct 4, 2023 EDT
建立圖像說明生成模型
Earned Oct 4, 2023 EDT
Transformer 和 BERT 模型
Earned Oct 4, 2023 EDT
注意力機制
Earned Oct 4, 2023 EDT
編碼器-解碼器架構
Earned Oct 4, 2023 EDT
圖像生成簡介
Earned Oct 4, 2023 EDT
負責任的 AI 技術簡介
Earned Oct 4, 2023 EDT
大型語言模型簡介
Earned Oct 4, 2023 EDT
生成式 AI 簡介
Earned Oct 2, 2023 EDT
為 Looker 資訊主頁和報表準備資料
Earned Oct 5, 2022 EDT
透過 Vertex AI 建構及部署機器學習解決方案
Earned Oct 2, 2022 EDT
Learn to Earn Cloud Challenge: Data
Earned Oct 3, 2021 EDT
Learn to Earn Cloud Challenge: Essentials
Earned Oct 1, 2021 EDT
Automate Interactions with Contact Center AI
Earned Apr 3, 2021 EDT
Anthos: Service Mesh
Earned Mar 31, 2021 EDT
DEPRECATED Application Development - Python
Earned Mar 30, 2021 EDT
在 Google Cloud 部署 Kubernetes 應用程式
Earned Mar 30, 2021 EDT
透過 Google Cloud Observability 監控及記錄系統狀態
Earned Mar 30, 2021 EDT
DEPRECATED Google Cloud's Operations Suite
Earned Mar 28, 2021 EDT
在 Google Cloud 實作 DevOps 工作流程
Earned Mar 28, 2021 EDT
[DEPRECATED] Build Interactive Apps with Google Assistant
Earned Mar 28, 2021 EDT
Google Cloud 中的 Kubernetes
Earned Mar 21, 2021 EDT
DEPRECATED Exploring APIs
Earned Mar 20, 2021 EDT
DEPRECATED ASP.NET on Google Cloud
Earned Mar 20, 2021 EDT
建立 Google Cloud 網路
Earned Mar 19, 2021 EDT
使用 Firebase 開發無伺服器應用程式
Earned Mar 18, 2021 EDT
在 Compute Engine 導入 Cloud Load Balancing
Earned Mar 16, 2021 EDT
在 Google Cloud 設定應用程式開發環境
Earned Mar 16, 2021 EDT
透過 Google Cloud 建構網站
Earned Mar 16, 2021 EDT
從 BigQuery 資料取得深入分析結果
Earned Mar 16, 2021 EDT
Getting Started with Go on Google Cloud
Earned Mar 16, 2021 EDT
DEPRECATED Websites and Web Applications
Earned Mar 14, 2021 EDT
Build Apps & Websites with Firebase
Earned Mar 13, 2021 EST
DEPRECATED Applied Data: Blockchain
Earned Aug 30, 2020 EDT
DEPRECATED BigQuery Basics for Data Analysts
Earned Aug 30, 2020 EDT
DEPRECATED BigQuery for Marketing Analysts
Earned Aug 30, 2020 EDT
Baseline: Deploy & Develop
Earned Aug 29, 2020 EDT
Google Workspace Essentials
Earned Aug 22, 2020 EDT
Google Developer Essentials
Earned Aug 22, 2020 EDT
[DEPRECATED] OK Google: Build Interactive Apps with Google Assistant
Earned Aug 22, 2020 EDT
使用 BigQuery 進行機器學習
Earned Aug 19, 2020 EDT
Advanced ML: ML Infrastructure
Earned Aug 19, 2020 EDT
DEPRECATED Google Cloud Solutions II: Data and Machine Learning
Earned Aug 16, 2020 EDT
[DEPRECATED] Data Engineering
Earned Aug 16, 2020 EDT
Machine Learning APIs
Earned Aug 15, 2020 EDT
Intro to ML: Image Processing
Earned Aug 15, 2020 EDT
機器學習簡介：語言處理
Earned Aug 13, 2020 EDT
Intermediate ML: TensorFlow on Google Cloud
Earned Aug 11, 2020 EDT
Google Cloud Essentials
Earned Aug 9, 2020 EDT
基本概念：資料、機器學習和 AI
Earned Aug 9, 2020 EDT
基本概念：基礎架構
Earned Aug 9, 2020 EDT
DEPRECATED BigQuery for Data Warehousing
Earned Aug 8, 2020 EDT