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Apply your skills in Google Cloud console

Smit Shah

成为会员时间：2019

钻石联赛

16200 积分
Google DeepMind: 01 Build Your Own Small Language Model Earned Apr 18, 2026 EDT
Gemini 和 Imagen 實務應用：建構 AI 應用程式 Earned May 4, 2025 EDT
Build a Certification Study Guide: PMLE Earned Mar 30, 2025 EDT
在 Vertex AI 設計提示 Earned Mar 30, 2025 EDT
負責任的 AI 技術簡介 Earned Mar 13, 2025 EDT
大型語言模型簡介 Earned Feb 9, 2025 EST
生成式 AI 簡介 Earned Feb 9, 2025 EST
建構安全的 Google Cloud 網路 Earned Mar 26, 2021 EDT
[DEPRECATED] Build Interactive Apps with Google Assistant Earned Mar 26, 2021 EDT
在 Google Cloud 部署 Kubernetes 應用程式 Earned Mar 21, 2021 EDT
雲端架構：設計、實作與管理 Earned Mar 16, 2021 EDT
透過 Google Cloud Observability 監控及記錄系統狀態 Earned Mar 14, 2021 EDT
在 Google Cloud 實作 DevOps 工作流程 Earned Mar 11, 2021 EST
使用 Firebase 開發無伺服器應用程式 Earned Mar 8, 2021 EST
透過 Google Cloud 建構網站 Earned Mar 7, 2021 EST
Getting Started with Go on Google Cloud Earned Mar 7, 2021 EST
在 Google Cloud 設定應用程式開發環境 Earned Mar 7, 2021 EST
在 Google Cloud 實作 Cloud 安全防護措施：基礎知識 Earned Mar 6, 2021 EST
在 Compute Engine 導入 Cloud Load Balancing Earned Mar 6, 2021 EST
Serverless Web Apps using Amazon DynamoDB Earned Oct 13, 2020 EDT
Solutions Architect - Professional Earned Oct 13, 2020 EDT
Solutions Architect - Associate Earned Oct 12, 2020 EDT
從 BigQuery 資料取得深入分析結果 Earned Oct 11, 2020 EDT
Data Catalog Fundamentals Earned Oct 11, 2020 EDT
DEPRECATED IoT in the Google Cloud Earned Oct 11, 2020 EDT
使用 BigQuery ML 為預測模型進行資料工程 Earned Oct 10, 2020 EDT
Intermediate ML: TensorFlow on Google Cloud Earned Oct 10, 2020 EDT
在 Google Cloud 為機器學習 API 準備資料 Earned Oct 10, 2020 EDT
Deployment & Management Earned Oct 9, 2020 EDT
Serverless Design with AWS Lambda Earned Oct 9, 2020 EDT
Scientific Data Processing Earned Oct 9, 2020 EDT
機器學習簡介：語言處理 Earned Oct 8, 2020 EDT
Security on AWS Earned Oct 8, 2020 EDT
使用 BigQuery 進行機器學習 Earned Oct 8, 2020 EDT
DEPRECATED Google Cloud Solutions II: Data and Machine Learning Earned Oct 7, 2020 EDT
DEPRECATED BigQuery for Data Warehousing Earned Oct 6, 2020 EDT
Intro to ML: Image Processing Earned Oct 6, 2020 EDT

In this Google DeepMind course, you will learn the fundamentals of language models and gain a high-level understanding of the machine learning development pipeline. You will consider the strengths and limitations of traditional n-gram models and advanced transformer models. Practical coding labs will enable you to develop insights into how machine learning models work and how they can be used to generate text and identify patterns in language. Through real-world case studies, you will build an understanding around how research engineers operate. Drawing on these insights you will identify problems that you wish to tackle in your own community and consider how to leverage the power of machine learning responsibly to address these problems within a global and local context.

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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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Learn how to use NotebookLM to create a personalized study guide for the Professional Machine Learning Engineer certification exam (PMLE). You'll review NotebookLM features, create a notebook, and use the study guide to practice for a certification exam.

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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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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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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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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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完成 在 Google Cloud 部署 Kubernetes 應用程式 技能徽章中階課程，即可證明您具備下列技能： 設定及建構 Docker 容器映像檔、建立及管理 Google Kubernetes Engine (GKE) 叢集、運用 kubectl 有效 管理叢集，以及運用強大的持續推送軟體更新做法來部署 Kubernetes 應用程式。

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完成 雲端架構：設計、實作與管理 課程即可獲得 技能徽章，證明您具備下列技能： 使用 Apache 網路伺服器部署可公開存取的網站、使用開機指令碼設定 Compute Engine VM、 使用 Windows 防禦主機和防火牆規則設定安全的 RDP、建構 Docker 映像檔並部署至 Kubernetes 叢集，然後進行更新，以及建立 Cloud SQL 執行個體並匯入 MySQL 資料庫。 這個技能徽章課程是絕佳的 資源，可讓您瞭解Google Cloud 認證專業雲端架構師認證測驗涵蓋的主題。

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完成 透過 Google Cloud Observability 監控及記錄系統狀態 技能徽章入門課程， 即可證明您具備下列技能：監控 Compute Engine 中的虛擬機器、 運用 Cloud Monitoring 監管多項專案、在 Cloud Functions 延伸應用監控和記錄功能、 建立和傳送自訂應用程式指標，以及根據自訂指標設定 Cloud Monitoring 快訊。

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完成 在 Google Cloud 實作 DevOps 工作流程 技能徽章中階課程， 即可證明您具備下列技能：使用 Cloud Source Repositories 建立 Git 存放區、 在 Google Kubernetes Engine (GKE) 發布、管理和調度 Deployment， 以及建立 CI/CD 管道，自動建構容器映像檔與執行 GKE 部署作業。

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完成 使用 Firebase 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：使用 Firebase 架構及建構無伺服器的網頁應用程式、 運用 Firestore 管理資料庫、使用 Cloud Build 自動部署內容， 以及將 Google 助理功能整合至應用程式。

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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

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Get started with Go (Golang) by reviewing Go code, and then creating and deploying simple Go apps on Google Cloud. Go is an open source programming language that makes it easy to build fast, reliable, and efficient software at scale. Go runs native on Google Cloud, and is fully supported on Google Kubernetes Engine, Compute Engine, App Engine, Cloud Run, and Cloud Functions. Go is a compiled language and is faster and more efficient than interpreted languages. As a result, Go requires no installed runtime like Node, Python, or JDK to execute.

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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 在 Google Cloud 實作 Cloud 安全防護措施：基礎知識 技能徽章中階課程， 即可證明您具備下列技能：運用 Identity and Access Management (IAM) 建立及指派角色、 建立及管理服務帳戶、啟用虛擬私有雲 (VPC) 網路中的私人連線、 運用 Identity-Aware Proxy 限制應用程式存取權、 運用 Cloud Key Management Service (KMS) 管理金鑰和已加密資料，以及建立私人 Kubernetes 叢集。

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完成「在 Compute Engine 導入 Cloud Load Balancing」技能徽章入門課程，即可證明您具備下列技能： 在 Compute Engine 建立及部署虛擬機器， 以及設定網路和應用程式負載平衡器。

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Serverless architectures allow you to build and run applications and services without needing to provision, manage, and scale infrastructure. This quest will show how to design, build, and deploy interactive serverless web applications, using a simple HTML/JavaScript web interface which uses Amazon API Gateway calls to send requests to AWS Lambda backends that query Amazon DynamoDB data.

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Achieving AWS Certification requires hands-on experience. This quest helps you get hands-on practice with several key services as you prepare for the AWS Certified Solutions Architect – Professional Exam. Visit AWS Certification to learn more about this exam and find more resources to prepare.

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Achieving AWS Certification requires hands-on experience. This quest helps you get hands-on practice with several key services as you prepare for the AWS Certified Solutions Architect – Associate Exam. Visit AWS Certification to learn more about this exam and find more resources to prepare.

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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 Looker Studio 中 透過連結 BigQuery 資料建立報表。

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Data Catalog is deprecated and will be discontinued on January 30, 2026. You can still complete this course if you want to. For steps to transition your Data Catalog users, workloads, and content to Dataplex Catalog, see Transition from Data Catalog to Dataplex Catalog (https://cloud.google.com/dataplex/docs/transition-to-dataplex-catalog). Data Catalog is a fully managed and scalable metadata management service that empowers organizations to quickly discover, understand, and manage all of their data. In this quest you will start small by learning how to search and tag data assets and metadata with Data Catalog. After learning how to build your own tag templates that map to BigQuery table data, you will learn how to build MySQL, PostgreSQL, and SQLServer to Data Catalog Connectors.

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In this quest, you will learn about Google Cloud’s IoT Core service and its integration with other services like GCS, Dataprep, Stackdriver and Firestore. The labs in this quest use simulator code to mimic IOT devices and the learning here should empower you to implement the same streaming pipeline with real world IoT devices.

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完成使用 BigQuery ML 為預測模型進行資料工程技能徽章中階課程， 即可證明自己具備下列知識與技能：運用 Dataprep by Trifacta 建構連至 BigQuery 的資料轉換 pipeline； 使用 Cloud Storage、Dataflow 和 BigQuery 建構「擷取、轉換及載入」(ETL) 工作負載， 以及使用 BigQuery ML 建構機器學習模型。

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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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完成 在 Google Cloud 為機器學習 API 準備資料 技能徽章入門課程，即可證明您具備下列技能： 使用 Dataprep by Trifacta 清理資料、在 Dataflow 執行資料管道、在 Dataproc 建立叢集和執行 Apache Spark 工作，以及呼叫機器學習 API，包含 Cloud Natural Language API、Google Cloud Speech-to-Text API 和 Video Intelligence API。

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In this quest, you’ll learn to work with services related to Deployment and Management, including AWS Identity and Access Management (IAM), AWS Elastic Beanstalk, AWS CloudFormation, and AWS OpsWorks.

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In this Quest, you will learn how to write functions with the AWS Lambda Service that respond to events and integrate other AWS Services. You will create applications that write records to Amazon DynamoDB, send messages with Amazon SNS, and monitor events in Amazon CloudWatch and external services. You will even write a back-end function in Lambda for creating a voice-response app for Alexa and the Amazon Echo.

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Big data, machine learning, and scientific data? It sounds like the perfect match. In this advanced-level quest, you will get hands-on practice with GCP services like Big Query, Dataproc, and Tensorflow by applying them to use cases that employ real-life, scientific data sets. By getting experience with tasks like earthquake data analysis and satellite image aggregation, Scientific Data Processing will expand your skill set in big data and machine learning so you can start tackling your own problems across a spectrum of scientific disciplines.

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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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This quest is designed to teach you how to apply AWS Identity and Access Management, in concert with several other AWS Services, to address real-world application and service security management scenarios.

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不想花費大把時間，想在幾分鐘內只靠 SQL，就建立好機器學習模型嗎？透過 BigQuery ML，資料分析師可以運用現有的 SQL 工具和技巧，建立、訓練、評估模型， 並使用模型進行預測，降低機器學習的使用門檻。在 本系列的實驗室，您會測試不同類型的模型，瞭解 優良模型應具備的條件。

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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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Looking to build or optimize your data warehouse? Learn best practices to Extract, Transform, and Load your data into Google Cloud with BigQuery. In this series of interactive labs you will create and optimize your own data warehouse using a variety of large-scale BigQuery public datasets. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights. Looking for a hands on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, enroll in and finish the additional challenge lab at the end of this quest to receive an exclusive Google Cloud digital badge.

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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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