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Kurt Schwem

Member since 2017

Silver League

1300 points
Serverless Web Apps using Amazon DynamoDB Earned May 14, 2020 EDT
Google Cloud 中的 Kubernetes Earned Mar 24, 2019 EDT
Baseline: Deploy & Develop Earned Mar 21, 2019 EDT
Machine Learning APIs Earned Mar 13, 2019 EDT
基本概念：資料、機器學習和 AI Earned Mar 5, 2019 EST
Google Cloud 必備知識 Earned Feb 25, 2019 EST
Security on AWS Earned May 3, 2017 EDT
Deployment & Management Earned Apr 27, 2017 EDT
Alexa Skills Development Earned Apr 20, 2017 EDT

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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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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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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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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在這堂入門課程，您將實際練習使用 Google Cloud 的基礎工具和服務。本課程包含可選擇觀賞的影片， 針對實驗室涵蓋的概念提供更多背景資訊，協助您複習。「Google Cloud 必備知識」 是適合 Google Cloud 學員的第一堂課， 即使您尚未學習或不熟悉雲端知識， 也能從這堂課獲得實務經驗，並應用於第一項 Google Cloud 專案。不管是撰寫 Cloud Shell 指令 和部署第一部虛擬機器，還是在 Kubernetes Engine 或透過負載平衡執行應用程式， 「Google Cloud 必備知識」都是認識平台基本功能的最佳入門資源。

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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’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 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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