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Michel LeBlond

成为会员时间：2018

黄金联赛

23265 积分
使用生成式 playbook 创建智能体 Earned Nov 13, 2024 EST
用 Cloud Vision API 分析图片 Earned Sep 17, 2024 EDT
矢量搜索和嵌入 Earned Sep 8, 2024 EDT
[DEPRECATED] 适用于端到端 SDLC 的 Gemini Earned Sep 7, 2024 EDT
[DEPRECATED] 适用于 DevOps 工程师的 Gemini Earned Sep 6, 2024 EDT
[DEPRECATED] 适用于网络工程师的 Gemini Earned Sep 3, 2024 EDT
[DEPRECATED] 面向数据科学家和分析师的 Gemini Earned Sep 2, 2024 EDT
[DEPRECATED] 适用于安全工程师的 Gemini Earned Aug 29, 2024 EDT
[DEPRECATED] 适用于云架构师的 Gemini Earned Jun 5, 2024 EDT
Transformer 模型和 BERT 模型 Earned Jun 2, 2024 EDT
编码器-解码器架构 Earned Jun 2, 2024 EDT
[DEPRECATED] 适用于应用开发者的 Gemini Earned Jun 2, 2024 EDT
注意力机制 Earned Jun 1, 2024 EDT
图像生成简介 Earned Jun 1, 2024 EDT
使用 Gemini 和 Streamlit 开发生成式 AI 应用 Earned May 31, 2024 EDT
Responsible AI: 和 Google Cloud 一起践行 AI 原则 Earned May 24, 2024 EDT
负责任的 AI 简介 Earned May 23, 2024 EDT
生成式 AI 简介 Earned May 23, 2024 EDT
在 Agent Platform 中设计提示 Earned May 22, 2024 EDT
大型语言模型简介 Earned Apr 30, 2024 EDT
[DEPRECATED] 适用于应用开发者的 Gemini Earned Mar 26, 2024 EDT
Cloud Hero Speedrun: Leap Day Earned Feb 28, 2020 EST
Valentine's Speedrun Earned Feb 16, 2020 EST
云工程 Earned Jan 3, 2020 EST
Google Cloud Run Serverless Workshop Earned Dec 21, 2019 EST
Using the Cloud SDK Command Line Earned Nov 11, 2019 EST
DevOps 基础知识 Earned Oct 12, 2019 EDT
Intro to ML: Image Processing Earned Oct 11, 2019 EDT
机器学习简介：语言处理 Earned Oct 10, 2019 EDT
[Deprecated] Advanced ML: ML Infrastructure Earned Oct 9, 2019 EDT
DEPRECATED BigQuery for Marketing Analysts Earned Oct 5, 2019 EDT
Intermediate ML: TensorFlow on Google Cloud Earned Oct 4, 2019 EDT
Cloud Development Earned Oct 2, 2019 EDT
DEPRECATED BigQuery for Data Warehousing Earned Sep 27, 2019 EDT
Google Developer Essentials Earned Sep 11, 2019 EDT
面向机器学习的 BigQuery Earned Sep 11, 2019 EDT
DEPRECATED BigQuery Basics for Data Analysts Earned Sep 11, 2019 EDT
[DEPRECATED] OK Google: Build Interactive Apps with Google Assistant Earned Apr 8, 2019 EDT
DevZone Quest Earned Apr 8, 2019 EDT
NCAA® March Madness®: Bracketology with Google Cloud Earned Apr 1, 2019 EDT
DEPRECATED Network Performance and Optimization Earned Jan 24, 2019 EST
DEPRECATED IoT in the Google Cloud Earned Jan 21, 2019 EST
DEPRECATED BigQuery for Data Analysis Earned Jan 3, 2019 EST
Managing Cloud Infrastructure with Terraform Earned Jan 3, 2019 EST
DEPRECATED Application Development - Python Earned Dec 20, 2018 EST
Data Science on Google Cloud Earned Dec 12, 2018 EST
DEPRECATED Developing Data and Machine Learning Apps with C# Earned Dec 11, 2018 EST
DEPRECATED Exploring APIs Earned Dec 10, 2018 EST
DEPRECATED Application Development - Java Earned Dec 9, 2018 EST
[Deprecated] Data Science on Google Cloud: Machine Learning Earned Nov 29, 2018 EST
Cloud SQL Earned Nov 29, 2018 EST
Deprecated Kubernetes Solutions Earned Nov 24, 2018 EST
Workspace: Add-ons Earned Nov 13, 2018 EST
Databases on AWS for Windows Earned Jul 13, 2018 EDT
Security on AWS Earned Jul 10, 2018 EDT
Alexa Skills Development Earned Jul 10, 2018 EDT
Deployment & Management Earned Jun 29, 2018 EDT
安全与身份基础知识 Earned Jun 21, 2018 EDT
Storage & CDN Earned May 17, 2018 EDT
SysOps Administrator - Associate Earned May 15, 2018 EDT
Solutions Architect - Professional Earned May 13, 2018 EDT
Serverless Web Apps using Amazon DynamoDB Earned Apr 27, 2018 EDT
Serverless Design with AWS Lambda Earned Apr 27, 2018 EDT
Solutions Architect - Associate Earned Apr 27, 2018 EDT
Compute & Networking Earned Apr 27, 2018 EDT
Developer - Associate Earned Apr 25, 2018 EDT
Corporate Apps on AWS for Windows Earned Apr 24, 2018 EDT
Digital Media Earned Apr 21, 2018 EDT
Websites & Web Apps Earned Apr 21, 2018 EDT
Automate Deployment and Manage Traffic on a Google Cloud Network Earned Apr 19, 2018 EDT
DEPRECATED Google Cloud Solutions II: Data and Machine Learning Earned Apr 18, 2018 EDT
[DEPRECATED] Data Engineering Earned Apr 16, 2018 EDT
DEPRECATED Cloud Architecture Earned Apr 16, 2018 EDT
Scientific Data Processing Earned Apr 9, 2018 EDT
Google Cloud Solutions I: Scaling Your Infrastructure Earned Apr 7, 2018 EDT
DEPRECATED Websites and Web Applications Earned Apr 6, 2018 EDT
Deployment Manager Earned Apr 5, 2018 EDT
DEPRECATED Windows on Google Cloud Earned Apr 3, 2018 EDT
Machine Learning APIs Earned Apr 2, 2018 EDT
基准：数据、机器学习和 AI Earned Apr 2, 2018 EDT
[DEPRECATED] Deploying Applications Earned Mar 30, 2018 EDT
在 Google Cloud 中使用 Kubernetes Earned Mar 28, 2018 EDT
DEPRECATED Google Cloud's Operations Suite Earned Mar 22, 2018 EDT
Baseline: Deploy & Develop Earned Mar 21, 2018 EDT
基准：基础架构 Earned Mar 16, 2018 EDT
Google Cloud 基础知识 Earned Mar 13, 2018 EDT

在本课程中，您将学习如何利用生成式 playbook 为 Conversational Agents 构建出色的对话体验。您首先会了解 playbook 的大致情况，然后学习如何设置您的首个 playbook。您还将了解测试的重要性，以及配额限制和集成等关键生产注意事项。课程最后将通过一个案例研究展示如何利用 playbook 改变内容生成方向。

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完成用 Cloud Vision API 分析图片课程，赢取技能徽章。在此挑战任务中，您将了解如何利用 Cloud Vision API 执行各种任务，包括从图片中提取文本。

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在本次课程中，探索 AI 赋能的搜索技术、工具和应用。学习利用向量嵌入的语义搜索、融合语义和关键字的混合搜索方法，以及检索增强生成 (RAG) 技术，以打造基于事实的 AI 智能体，尽可能减少 AI 幻觉。获取 Vertex AI Vector Search 实战经验，打造您自己的智能搜索引擎。

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在本课程中，您将了解 Gemini（Google Cloud 推出的一款依托生成式 AI 的协作工具）如何帮助您使用 Google 产品和服务开发、测试、部署和管理应用。在 Gemini 的协助下，您可以学习如何开发和构建 Web 应用、修复应用中的错误、开发测试和查询数据。您可以通过实操实验了解如何利用 Gemini 来改进软件开发生命周期 (SDLC)。 Duet AI 已更名为 Gemini，这是我们的新一代模型。

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在本课程中，您将了解 Gemini（Google Cloud 推出的一款依托生成式 AI 的协作工具）如何帮助工程师管理基础设施。您将了解如何向 Gemini 输入提示，让其查找和理解应用日志、创建 GKE 集群，以及研究如何创建构建环境。您可以通过实操实验了解如何利用 Gemini 来改进 DevOps 工作流。 Duet AI 已更名为 Gemini，这是我们的新一代模型。

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在本课程中，您将了解 Gemini（Google Cloud 推出的一款依托生成式 AI 的协作工具）如何帮助网络工程师创建、更新和维护 VPC 网络。您将学习如何向 Gemini 输入提示，让其针对您的网络组建和管理任务，提供您从搜索引擎所无法获得的具体指导。您可以通过实操实验了解如何利用 Gemini 更轻松地使用 Google Cloud VPC 网络。 Duet AI 已更名为 Gemini，这是我们的新一代模型。

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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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在本课程中，您将了解 Gemini（Google Cloud 推出的一款依托生成式 AI 的协作工具）如何帮助您保护您的云环境和资源。您将学习如何将示例工作负载部署到 Google Cloud 环境中，以及如何借助 Gemini 识别和修复安全配置错误。您可以通过实操实验了解如何利用 Gemini 来改善云安全状况。 Duet AI 已更名为 Gemini，这是我们的新一代模型。

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在本课程中，您将了解 Gemini（Google Cloud 的生成式 AI 赋能的协作工具）如何帮助管理员预配基础设施。您将了解如何通过输入提示来让 Gemini 解释基础设施、GKE 集群的部署，以及现有基础设施的更新。您可以通过实操实验了解如何利用 Gemini 来改进 GKE 部署工作流。 Duet AI 已更名为 Gemini，这是我们的新一代模型。

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本课程向您介绍 Transformer 架构和 Bidirectional Encoder Representations from Transformers (BERT) 模型。您将了解 Transformer 架构的主要组成部分，例如自注意力机制，以及该架构如何用于构建 BERT 模型。您还将了解可以使用 BERT 的不同任务，例如文本分类、问答和自然语言推理。完成本课程估计需要大约 45 分钟。

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本课程简要介绍了编码器-解码器架构，这是一种功能强大且常见的机器学习架构，适用于机器翻译、文本摘要和问答等 sequence-to-sequence 任务。您将了解编码器-解码器架构的主要组成部分，以及如何训练和部署这些模型。在相应的实验演示中，您将在 TensorFlow 中从头编写简单的编码器-解码器架构实现代码，以用于诗歌生成。

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在本课程中，您将了解 Google Cloud 中依托生成式 AI 技术的协作工具 Gemini 如何帮助开发者构建应用。您将学习如何向 Gemini 输入提示，让其为您解释代码、推荐 Google Cloud 服务并为您的应用生成代码。您将通过实操实验体验 Gemini 对应用开发工作流的改进作用。 Duet AI 已更名为 Gemini，这是我们的新一代模型。

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本课程将向您介绍注意力机制，这是一种强大的技术，可令神经网络专注于输入序列的特定部分。您将了解注意力的工作原理，以及如何使用它来提高各种机器学习任务的性能，包括机器翻译、文本摘要和问题解答。

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本课程向您介绍扩散模型。这类机器学习模型最近在图像生成领域展现出了巨大潜力。扩散模型的灵感来源于物理学，特别是热力学。过去几年内，扩散模型成为热门研究主题并在整个行业开始流行。Google Cloud 上许多先进的图像生成模型和工具都是以扩散模型为基础构建的。本课程向您介绍扩散模型背后的理论，以及如何在 Vertex AI 上训练和部署此类模型。

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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。

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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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这是一节入门级微课程，旨在解释什么是负责任的 AI、它的重要性，以及 Google 如何在自己的产品中实现负责任的 AI。此外，本课程还介绍了 Google 的 7 个 AI 开发原则。

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这是一节入门级微课程，旨在解释什么是生成式 AI、它的用途以及与传统机器学习方法的区别。该课程还介绍了可以帮助您开发自己的生成式 AI 应用的各种 Google 工具。

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完成 在 Agent Platform 中设计提示入门技能徽章课程，展示以下方面的技能： Agent Platform 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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这是一节入门级微学习课程，探讨什么是大型语言模型 (LLM)、适合的应用场景以及如何使用提示调整来提升 LLM 性能，还介绍了可以帮助您开发自己的 Gen AI 应用的各种 Google 工具。

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在本课程中，您将了解 Google Cloud 中依托生成式 AI 技术的协作工具 Gemini 如何帮助开发者构建应用。您将学习如何向 Gemini 输入提示，让其为您解释代码、推荐 Google Cloud 服务并为您的应用生成代码。您将通过实操实验体验 Gemini 对应用开发工作流的改进作用。 Duet AI 已更名为 Gemini，这是我们的新一代模型。

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Welcome Gamers! Are you a developer? Get hands-on practice with the state-of-the-art tools for developing applications in Google Cloud. The skills you learn align with the Professional Cloud Developer Certification exam. You will work with two different languages, Java and Python, and get 4 labs closer to earning the Cloud Development badge. Complete the game labs one by one to score points. Earn the most points by completing the steps in the lab.... and get bonus points for speed! Be sure to click "End" when you're done with each lab to get the maximum points. Ready to play?

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Come! Fall in love with some interesting cloud services this Valentine's day. Have fun with interesting labs and get to learn something new! Challenge yourself to complete each task as quickly and accurately as possible to score points and earn badges!

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在众多课程中，本入门课程独具特色。 这些实验经过精心设计，旨在让 IT 专业人员通过实践掌握 Google Cloud 认证 Associate Cloud Engineer 考核中的各项主题和服务内容。从 IAM 到网络组建和管理， 再到 Kubernetes Engine 部署，本课程将通过特定实验 检验您的 Google Cloud 知识掌握情况。请注意，虽然这些实操 实验有助于提升您的技能和能力，我们仍建议您同时查阅 考试指南和其他可用的备考资源。

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Twelve years ago Lily started the Pet Theory chain of veterinary clinics, and has been expanding rapidly. Now, Pet Theory is experiencing some growing pains: their appointment scheduling system is not able to handle the increased load, customers aren't receiving lab results reliably through email and text, and veteranerians are spending more time with insurance companies than with their patients. Lily wants to build a cloud-based system that scales better than the legacy solution and doesn't require lots of ongoing maintenance. The team has decided to go with serverless technology. For the labs in the Google Cloud Run Serverless Quest, you will read through a fictitious business scenario in each lab and assist the characters in implementing a serverless solution. 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…

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For everyone using Google Cloud Platform for the first time, getting familar with gcloud, Google Cloud's command line, will help you get up to speed faster. In this quest, you'll learn how to install and configure Cloud SDK, then use gcloud to perform some basic operations like creating VMs, networks, using BigQuery, and using gsutil to perform operations.

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通过 DevOps 获得 竞争优势。DevOps 是一种组织和文化运动，旨在加快软件交付速度， 提高服务可靠性，并在软件利益相关方之间 建立共享所有权。在本课程中，您将学习如何使用 Google Cloud 提高 软件交付能力，确保较高的速度、稳定性、可用性和安全性。 DevOps Research and Assessment 已加入 Google Cloud。您的团队表现如何？参加 这个包含 5 道选择题的测验，来一探究竟吧！

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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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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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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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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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The hands-on labs in this Quest are structured to give experienced app developers hands-on practice with the state-of-the-art developing applications in Google Cloud. The topics align with the Google Cloud Certified Professional Cloud Developer Certification. These labs follow the sequence of activities needed to create and deploy an app in Google Cloud from beginning to end. Be aware that while practice with these labs will increase your skills and abilities, it is recommended that you also review the exam guide and other available preparation resources.

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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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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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想要仅使用 SQL 就能在几分钟内构建机器学习模型，而不是花费数小时？BigQuery 借助机器学习，数据分析师能够使用现有的 SQL 工具和技能创建、训练、评估机器学习模型，并使用这些模型进行预测， 从而实现机器学习的普及。在 本系列实验中，您将尝试不同的模型类型，并了解 如何构建出色的模型。

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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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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.

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Welcome to DevZone Quest, a set of labs to deepen your understanding of the technology behind the Cloud Showcase Experiments featured in the Google Cloud Next 2019 San Francisco DevZone.

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In this series of labs you will learn how to use BigQuery to analyze NCAA basketball data with SQL. Build a Machine Learning Model to predict the outcomes of NCAA March Madness basketball tournament games.

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If you want to take your Google Cloud networking skills to the next level, look no further. This course is composed of labs that cover real-life use cases and it will teach you best practices for overcoming common networking bottlenecks. From getting hands-on practice with testing and improving network performance, to integrating high-throughput VPNs and networking tiers, Network Performance and Optimization is an essential course for Google Cloud developers who are looking to double down on application speed and robustness.

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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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Want to learn the core SQL and visualization skills of a Data Analyst? Interested in how to write queries that scale to petabyte-size datasets? Take the BigQuery for Analyst Quest and learn how to query, ingest, optimize, visualize, and even build machine learning models in SQL inside of BigQuery.

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In this Quest, the experienced user of Google Cloud will learn how to describe and launch cloud resources with Terraform, an open source tool that codifies APIs into declarative configuration files that can be shared amongst team members, treated as code, edited, reviewed, and versioned. In these nine hands-on labs, you will work with example templates and understand how to launch a range of configurations, from simple servers, through full load-balanced applications.

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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.

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This is the first of two Quests of hands-on labs is derived from the exercises from the book Data Science on Google Cloud Platform, 2nd Edition by Valliappa Lakshmanan, published by O'Reilly Media, Inc. In this first Quest, covering up through chapter 8, you are given the opportunity to practice all aspects of ingestion, preparation, processing, querying, exploring and visualizing data sets using Google Cloud tools and services.

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C# has powered Windows .NET application development for nearly two decades and Google Cloud is committed to supporting developers getting their .NET workloads up and running on Google Cloud. In this quest, you will learn how to run C# apps in Google Cloud, and specifically how to take your apps to the next level by interfacing them with the big data and machine learning APIs that are accessible now from C#. By enrolling in this quest you will see firsthand how seamlessly Google Cloud integrates with .NET workloads and what the possibilities are for leveraging big data and ML services in your own C# projects.

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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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In this advanced-level quest, you will learn the ins and outs of developing GCP applications in Java. 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 Java 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 Java applications straight away.

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This is the second of two courses of hands-on labs derived from the exercises from the book Data Science on Google Cloud Platform, 2nd Edition by Valliappa Lakshmanan, published by O'Reilly Media, Inc. In this second Quest, covering chapter 9 through the end of the book, you extend the skills practiced in the first Quest, and run full-fledged machine learning jobs with state-of-the-art tools and real-world data sets, all using Google Cloud tools and services.

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Cloud SQL is a fully managed database service that stands out from its peers due to high performance, seamless integration, and impressive scalability. In this quest you will receive hands-on practice with the basics of Cloud SQL and quickly progress to advanced features, which you will apply to production frameworks and application environments. From creating instances and querying data with SQL, to building Deployment Manager scripts and connecting Cloud SQL instances with applications run on GKE containers, this quest will give you the knowledge and experience needed so you can start integrating this service right away.

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Containerized applications have changed the game and are here to stay. With Kubernetes, you can orchestrate containers with ease, and integration with the Google Cloud Platform is seamless. In this advanced-level quest, you will be exposed to a wide range of Kubernetes use cases and will get hands-on practice architecting solutions over the course of 8 labs. From building Slackbots with NodeJS, to deploying game servers on clusters, to running the Cloud Vision API, Kubernetes Solutions will show you first-hand how agile and powerful this container orchestration system is.

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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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Learn how to deploy and administer databases running on Microsoft Windows Server in Amazon EC2 and Amazon RDS.

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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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In this Quest, you will learn how to create Alexa skills that respond to voice commands and which can be used on the Amazon Echo, Dot, and Tap devices. You will create back-end functions in AWS Lambda, and then connect them with voice response logic using the Alexa Skills Kit. You will use both the AWS Console and the Amazon Developer Portal in these labs, the latter requiring you to have or create a no-cost, no-credit-card-required account. No hardware device is required for any lab; an Alexa voice response simulation system is provided in the Amazon Developer Portal. Templates used in these labs can be adapted and extended to create your own Alexa skills and offer them to the worldwide Alexa user community.

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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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安全是 Google Cloud 服务绝不妥协的核心原则，为此， Google Cloud 开发了特定工具，为您的所有项目提供 安全与身份保障。本入门课程中，您将了解 用于管理用户和虚拟机账号的 Google Cloud Identity and Access Management (IAM) 服务 并进行实操练习。您还将通过 配置 VPC 和 VPN 获取网络安全方面的实践经验，并了解有哪些工具可用于 抵御安全威胁和防止数据泄露。

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In this Quest, you’ll learn to work with services related to Storage and Content Delivery Networks, including Amazon Simple Storage Service (S3), Amazon Elastic Block Store (EBS), and Amazon CloudFront.

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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 SysOps Administrator - Associate 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 – Professional Exam. Visit AWS Certification to learn more about this exam and find more resources to prepare.

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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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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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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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In this quest, you’ll learn to work with services related to Compute and Networking, including Amazon EC2, Amazon Elastic Load Balancing, and Amazon Virtual Private Cloud (VPC).

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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 Developer - Associate Exam. Visit AWS Certification to learn more about this exam and find more resources to prepare.

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Learn how to deploy Microsoft Windows Server-based applications in the AWS cloud, including Microsoft Exchange, Dynamics CRM and SharePoint.

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AWS provides a set of on-demand storage, archive, transcoding, and streaming services for businesses that are running photo, video, and file storage applications in the cloud. In this quest, you’ll learn to work with foundational services for digital media on AWS.

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AWS offers services that provide businesses with a flexible, highly scalable, and low-cost way to deliver their websites and web applications. In this quest, you’ll learn to work with foundational services for marketing websites on AWS.

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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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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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This fundamental-level quest is unique amongst the other quest 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 Cloud Architect Certification. From IAM, to networking, to Kubernetes engine deployment, this quest is composed of specific labs that will put your Google Cloud knowledge to the test. Be aware that while practice with these labs will increase your skills and abilities, we recommend that you also review the exam guide and other available preparation resources.

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Big data, machine learning, and scientific data? It sounds like the perfect match. In this course, you will get hands-on practice with GCP services like BigQuery, Managed Service for Apache Spark, 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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In this course you will learn how you to harness serious Google Cloud power and infrastructure. The hands-on labs will give you use cases and you will be tasked with implementing scaling practices utilized by Google’s very own Solutions Architecture team. From developing enterprise grade load balancing and autoscaling, to building continuous delivery pipelines, Google Cloud Solutions I: Scaling your Infrastructure will teach you best practices for taking your Google Cloud projects to the next level.

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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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If you’re looking to take your Google Cloud application to the next level, look no further than Deployment Manager. By automating the creation of GCP resources and services, Deployment Manager lets you focus on developing rather than maintaining. In this advanced-level quest, you will get hands on practice with Deployment Manager by building custom templates, automating Python and Jinja application instances, and scaling custom networks.

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Google Cloud is committed to supporting Windows workloads in its frameworks and services. In this advanced-level quest, you will get hands-on practice running many of the popular Windows services on Google Cloud. For example, you will learn how to instantiate Microsoft SQL databases, cloud tools for Powershell on Google Cloud Platform frameworks.

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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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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。

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The Google Cloud Platform provides many different frameworks and options to fit your application’s needs. In this introductory-level quest, you will get plenty of hands-on practice deploying sample applications on Google App Engine. You will also dive into other web application frameworks like Firebase, Wordpress, and Node.js and see firsthand how they can be integrated with Google Cloud.

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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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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.

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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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如果您是一位入门级云开发者， 在学习了“Google Cloud 基础知识”课程之后，想要寻求真正的实操机会，这门课程就是您的不二之选。您将获得宝贵的实操经验， 通过多个实验深入探索 Cloud Storage 以及 Monitoring 和 Cloud Functions 等其他关键应用服务。您将掌握一系列宝贵技能， 在 Google Cloud 的任何计划中，这些技能都能发挥作用。

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在本入门级课程中，您将了解 Google Cloud 的基础工具和服务。此课程提供了可选视频， 旨在帮助您深入了解和回顾实验中涉及的概念。Google Cloud 基础知识是推荐给 Google Cloud 学员的第一门课程 - 即使您几乎没有云相关知识，也能从中获得实践 经验，并将其直接运用于您的首个 Google Cloud 项目。从编写 Cloud Shell 命令和部署您的第一个虚拟机，到在 Kubernetes Engine 上运行应用 或者使用负载均衡，“Google Cloud 基础知识”都是您了解该平台 基本功能的首选入门级课程。

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