Responsible AI: 和 Google Cloud 一起践行 AI 原则
Earned Feb 7, 2024 EST
Generative AI Fundamentals - 简体中文
Earned Feb 6, 2024 EST
Vertex AI Studio 简介
Earned Oct 4, 2023 EDT
创建图片标注模型
Earned Oct 4, 2023 EDT
Transformer 模型和 BERT 模型
Earned Oct 4, 2023 EDT
注意力机制
Earned Oct 4, 2023 EDT
编码器-解码器架构
Earned Oct 4, 2023 EDT
图像生成简介
Earned Oct 4, 2023 EDT
负责任的 AI 简介
Earned Oct 4, 2023 EDT
大型语言模型简介
Earned Oct 4, 2023 EDT
生成式 AI 简介
Earned Oct 2, 2023 EDT
为 Looker 信息中心和报告准备数据
Earned Oct 5, 2022 EDT
在 Vertex AI 上构建和部署机器学习解决方案
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 上部署 Kubernetes 应用
Earned Mar 30, 2021 EDT
使用 Google Cloud Observability 进行监控和记录
Earned Mar 30, 2021 EDT
DEPRECATED Google Cloud's Operations Suite
Earned Mar 28, 2021 EDT
在 Google Cloud 中实施 DevOps 工作流
Earned Mar 28, 2021 EDT
[DEPRECATED] Build Interactive Apps with Google Assistant
Earned Mar 28, 2021 EDT
在 Google Cloud 中使用 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
开发 Google Cloud 网络
Earned Mar 19, 2021 EDT
借助 Firebase 开发无服务器应用
Earned Mar 18, 2021 EDT
为 Compute Engine 实现云负载均衡
Earned Mar 16, 2021 EDT
在 Google Cloud 上设置应用开发环境
Earned Mar 16, 2021 EDT
在 Google Cloud 上构建网站
Earned Mar 16, 2021 EDT
从 BigQuery 数据中挖掘数据洞见
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
面向机器学习的 BigQuery
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
机器学习简介：语言处理
Earned Aug 13, 2020 EDT
Intermediate ML: TensorFlow on Google Cloud
Earned Aug 11, 2020 EDT
Google Cloud Essentials
Earned Aug 9, 2020 EDT
基准：数据、机器学习和 AI
Earned Aug 9, 2020 EDT
基准：基础架构
Earned Aug 9, 2020 EDT
DEPRECATED BigQuery for Data Warehousing
Earned Aug 8, 2020 EDT
本课程介绍 AI 隐私保护和安全方面的重要主题，还将探索使用 Google Cloud 产品和开源工具实施建议的 AI 隐私保护和安全实践的实用方法和工具。
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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 Sensitive Data Protection 使用入门 这一入门级技能徽章课程，展示您在以下方面的技能：使用 Sensitive Data Protection 服务 （包括 Cloud Data Loss Prevention API）来检查和隐去 Google Cloud 中的敏感数据，并对敏感数据进行去标识处理。
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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.
在本课程中，您将了解 Gemini（Google Cloud 的生成式 AI 赋能的协作工具）如何帮助分析客户数据并预测产品销售情况。此外，您还将了解如何在 BigQuery 中使用客户数据来识别、开发新客户并对其进行分类。通过动手实验，您将体验 Gemini 如何改进数据分析和机器学习工作流。
Duet AI 已更名为 Gemini，这是我们的新一代模型。
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完成入门级技能徽章课程在 Google Cloud 上为机器学习 API 准备数据，展示以下技能： 使用 Dataprep by Trifacta 清理数据、在 Dataflow 中运行数据流水线、在 Dataproc 中创建集群和运行 Apache Spark 作业，以及调用机器学习 API，包括 Cloud Natural Language API、Google Cloud Speech-to-Text API 和 Video Intelligence API。
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完成中级技能徽章课程“使用 Gemini 和 Streamlit 开发生成式 AI 应用”，展示您在以下方面的技能： 文本生成、通过 Python SDK 和 Gemini API 应用函数调用，以及通过 Cloud Run 部署 Streamlit 应用。 您将了解如何以不同方式通过提示来让 Gemini 生成文本、使用 Cloud Shell 进行测试，以及如何迭代 Streamlit 应用，随后将其封装成 Docker 容器并部署在 Cloud Run 中。
完成中级技能徽章课程使用 Vertex AI 中的 Gemini API 探索生成式 AI，展示自己在以下方面的技能： 文本生成技能、用于增强内容创作能力的图像和视频分析技能，以及在 Gemini API 中应用函数调用技术的技能。 了解如何运用先进的 Gemini 技术、探索多模态内容生成方法，并扩展 AI 赋能项目的功能。
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此课程将探索如何使用 AI 功能套件 Gemini in BigQuery 为“数据到 AI”工作流提供助力。其中涉及到的功能包括数据探索和准备、代码生成和问题排查，以及工作流发现和可视化。此课程包含概念解释、真实使用场景以及实操实验等内容，可帮助数据从业者提升效率并加快流水线开发速度。
完成在 Google Cloud 上使用 Machine Learning API 课程，赢取高级技能徽章。 在本课程中，您将了解以下机器学习和 AI 技术的基本功能： Cloud Vision API、Cloud Translation API 和 Cloud Natural Language API。
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完成用 Document AI 实现大规模自动数据采集课程，赢取入门级技能徽章。在本课程中，您将学习如何使用 Document AI 提取、处理和采集数据。
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完成 Cloud Speech API：3 种使用方式课程，赢取入门级技能徽章。 在本课程中，您将了解如何使用语音相关 API 工具来合成和转写语音。
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完成“使用 Gemini 和 Imagen 构建实际 AI 应用”技能徽章入门课程，展示您在以下方面的技能：图像识别、自然语言处理、
使用 Google 强大的 Gemini 和 Imagen 模型生成图像、在 Vertex AI 平台上部署应用。
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完成 在 Vertex AI 中设计提示入门技能徽章课程，展示以下方面的技能： Vertex AI 中的提示工程、图片分析和多模态生成式技术。探索如何编写有效的提示，指导生成式 AI 输出， 以及将 Gemini 模型应用于真实的营销场景。
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探索生成式 AI - Vertex AI 课程汇集了多组实验， 指导用户在 Google Cloud 平台上运用生成式 AI。参与实验，您将了解 如何使用 Vertex AI PaLM API 系列模型，包括 text-bison、chat-bison 和 textembedding-gecko。您还将了解提示设计、最佳实践， 以及如何使用生成式 AI 进行构思、文本分类、文本提取、文本 总结等任务。您还将学习如何通过 Vertex AI 自定义训练对基础模型进行调优， 并将模型部署到 Vertex AI 端点。
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随着企业对人工智能和机器学习的应用越来越广泛，以负责任的方式构建这些技术也变得更加重要。但对很多企业而言，真正践行 Responsible AI 并非易事。如果您有意了解如何在组织内践行 Responsible AI，本课程正适合您。
本课程将介绍 Google Cloud 目前如何践行 Responsible AI，以及从中总结的最佳实践和经验教训，便于您以此为框架构建自己的 Responsible AI 方法。
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完成 Introduction to Generative AI、Introduction to Large Language Models 和 Introduction to Responsible AI 三门课程，赢取技能徽章。通过最终测验，即表明您理解了生成式 AI 的基本概念。
技能徽章是由 Google Cloud 颁发的数字徽章，旨在认可您对 Google Cloud 产品与服务的了解程度。公开您的个人资料并将技能徽章添加到您的社交媒体个人资料中，以此来分享您获得的成就。
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本课程介绍 Vertex AI Studio，这是一种用于与生成式 AI 模型交互、围绕业务创意进行原型设计并在生产环境中落地的工具。通过沉浸式应用场景、富有吸引力的课程和实操实验，您将探索从提示到产品的整个生命周期，了解如何将 Vertex AI Studio 用于多模态 Gemini 应用、提示设计、提示工程和模型调优。本课程的目的在于帮助您利用 Vertex AI Studio，在自己的项目中充分发掘生成式 AI 的潜力。
完成在 Vertex AI 上构建和部署机器学习解决方案课程，赢取中级技能徽章。 在此课程中，您将了解如何使用 Google Cloud 的 Vertex AI Platform、AutoML 以及自定义训练服务来 训练、评估、调优、解释和部署机器学习模型。 此技能徽章课程的目标受众是专业的数据科学家和机器学习 工程师。 技能徽章是由 Google Cloud 颁发的专属数字徽章，旨在认可 您对 Google Cloud 产品与服务的熟练度；您需要在 交互式实操环境中参加考核，证明自己运用所学知识的能力后才能获得此徽章。完成此技能徽章课程 和作为最终评估的实验室挑战赛，即可获得数字徽章， 在您的人际圈中炫出自己的技能。
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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 是最受欢迎的容器编排系统， Google Kubernetes Engine 专为支持 Google Cloud 中的托管式 Kubernetes 部署 而设计。在本高级课程中，您将亲自动手配置 Docker 映像、容器，并部署功能完备的 Kubernetes Engine 应用。 此课程将帮助您掌握在工作流中集成容器编排所需的 实用技能。 想要参加实操实验室挑战赛， 展示您的技能并检验所学知识？完成本课程后，不妨继续参与这项额外的 实验室挑战赛，赢得 Google Cloud 专属数字徽章。 该挑战赛位于在 Google Cloud 上部署 Kubernetes 应用课程的结尾处。
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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.
完成“在 Google Cloud 上设置应用开发环境”课程，赢取技能徽章；通过该课程，您将了解如何使用以下技术的基本功能来构建和连接以存储为中心的云基础设施： Cloud Storage、Identity and Access Management、Cloud Functions 和 Pub/Sub。
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完成在 Google Cloud 上构建网站技能徽章课程，赢取入门级技能徽章。本课程以 Get Cooking in Cloud 系列视频为基础， 涵盖以下主题：在 Cloud Run 上部署网站在 Compute Engine 上托管 Web 应用在 Google Kubernetes Engine 上创建、部署和扩缩网站使用 Cloud Build 从单体式应用迁移到微服务架构
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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众所周知，机器学习是发展最快的技术领域之一， Google Cloud Platform 在推动其发展方面发挥了重要作用。 GCP 提供了一系列 API，几乎可以满足任何机器学习作业的需求。在 本入门课程中，您将了解机器学习在语言处理方面的运用， 通过实操实验学习 如何从文本中提取实体，执行情感和语法分析，以及 使用 Speech-to-Text API 进行转写。
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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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在此入门级挑战任务中，您可以使用 Google Cloud Platform 的基本工具和服务，开展真枪实弹的操作实训。“GCP 基本功能”是我们为 Google Cloud 学员推荐的第一项挑战任务。云知识储备微乎其微甚至零基础？不用担心！这项挑战任务会为您提供真枪实弹的实操经验，助您快速上手 GCP 项目。无论是要编写 Cloud Shell 命令还是部署您的第一台虚拟机，亦或是通过负载平衡机制或在 Kubernetes Engine 上运行应用，都可以通过“GCP 基本功能”了解该平台的基本功能之精要。点此观看 1 分钟视频，了解每个实验涉及的主要概念。
了解详情
大数据、机器学习和人工智能是当今计算领域的热门话题， 但这些领域的专业性很强，因而很难找到 入门资料。幸运的是，Google Cloud 在这些领域提供了方便用户使用的服务， 通过本入门级课程，您可以 开始学习使用 BigQuery、Cloud Speech API 和 Video Intelligence 等工具。
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.
面向开发者的 Responsible AI：隐私保护和安全
Earned Oct 5, 2025 EDT
Vertex AI Search for Commerce
Earned Oct 5, 2025 EDT
在 BigQuery 中使用 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
Sensitive Data Protection 使用入门
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
使用 BigQuery 实现多模态向量搜索
Earned Sep 22, 2025 EDT
面向数据科学家和分析师的 Gemini
Earned Sep 22, 2025 EDT
在 Google Cloud 上为机器学习 API 准备数据
Earned Sep 22, 2025 EDT
使用 Gemini 和 Streamlit 开发生成式 AI 应用
Earned Sep 22, 2025 EDT
使用多模态 Gemini 和多模态 RAG 检查富文档
Earned Sep 22, 2025 EDT
使用 Vertex AI 中的 Gemini API 探索生成式 AI
Earned Sep 22, 2025 EDT
使用 Gemini in BigQuery 提高效率
Earned Sep 20, 2025 EDT
使用 Gemini 分析和推理多模态数据
Earned Sep 20, 2025 EDT
提升 Gemini 模型的能力
Earned Sep 19, 2025 EDT
保护 BigLake 数据
Earned Sep 19, 2025 EDT
Google Cloud 云计算基础知识
Earned Sep 19, 2025 EDT
在 Google Cloud 上使用 Machine Learning API
Earned Sep 19, 2025 EDT
用 Document AI 实现大规模自动数据采集
Earned Sep 19, 2025 EDT
Cloud Speech API：3 种使用方式
Earned Sep 18, 2025 EDT
使用 Gemini 和 Imagen 构建实用 AI 应用
Earned Sep 18, 2025 EDT
在 Vertex AI 中设计提示
Earned Sep 18, 2025 EDT
探索生成式 AI - Vertex AI
Earned Mar 29, 2024 EDT
Responsible AI: 和 Google Cloud 一起践行 AI 原则
Earned Feb 7, 2024 EST
Generative AI Fundamentals - 简体中文
Earned Feb 6, 2024 EST
Vertex AI Studio 简介
Earned Oct 4, 2023 EDT
创建图片标注模型
Earned Oct 4, 2023 EDT
Transformer 模型和 BERT 模型
Earned Oct 4, 2023 EDT
注意力机制
Earned Oct 4, 2023 EDT
编码器-解码器架构
Earned Oct 4, 2023 EDT
图像生成简介
Earned Oct 4, 2023 EDT
负责任的 AI 简介
Earned Oct 4, 2023 EDT
大型语言模型简介
Earned Oct 4, 2023 EDT
生成式 AI 简介
Earned Oct 2, 2023 EDT
为 Looker 信息中心和报告准备数据
Earned Oct 5, 2022 EDT
在 Vertex AI 上构建和部署机器学习解决方案
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 上部署 Kubernetes 应用
Earned Mar 30, 2021 EDT
使用 Google Cloud Observability 进行监控和记录
Earned Mar 30, 2021 EDT
DEPRECATED Google Cloud's Operations Suite
Earned Mar 28, 2021 EDT
在 Google Cloud 中实施 DevOps 工作流
Earned Mar 28, 2021 EDT
[DEPRECATED] Build Interactive Apps with Google Assistant
Earned Mar 28, 2021 EDT
在 Google Cloud 中使用 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
开发 Google Cloud 网络
Earned Mar 19, 2021 EDT
借助 Firebase 开发无服务器应用
Earned Mar 18, 2021 EDT
为 Compute Engine 实现云负载均衡
Earned Mar 16, 2021 EDT
在 Google Cloud 上设置应用开发环境
Earned Mar 16, 2021 EDT
在 Google Cloud 上构建网站
Earned Mar 16, 2021 EDT
从 BigQuery 数据中挖掘数据洞见
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
面向机器学习的 BigQuery
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
机器学习简介：语言处理
Earned Aug 13, 2020 EDT
Intermediate ML: TensorFlow on Google Cloud
Earned Aug 11, 2020 EDT
Google Cloud Essentials
Earned Aug 9, 2020 EDT
基准：数据、机器学习和 AI
Earned Aug 9, 2020 EDT
基准：基础架构
Earned Aug 9, 2020 EDT
DEPRECATED BigQuery for Data Warehousing
Earned Aug 8, 2020 EDT