Ashutosh Krishna
Member since 2019
Silver League
2800 points
在 Google Cloud 部署 Kubernetes 應用程式
Earned Nov 16, 2020 EST
在 Compute Engine 導入 Cloud Load Balancing
Earned Nov 14, 2020 EST
[DEPRECATED] Build Interactive Apps with Google Assistant
Earned Nov 14, 2020 EST
透過 Google Cloud Observability 監控及記錄系統狀態
Earned Nov 14, 2020 EST
Scientific Data Processing
Earned Oct 23, 2019 EDT
[DEPRECATED] Data Engineering
Earned Oct 21, 2019 EDT
Data Science on Google Cloud
Earned Oct 21, 2019 EDT
DEPRECATED BigQuery for Data Analysis
Earned Oct 20, 2019 EDT
基本概念：資料、機器學習和 AI
Earned Oct 16, 2019 EDT
[DEPRECATED] OK Google: Build Interactive Apps with Google Assistant
Earned Oct 14, 2019 EDT
Google Workspace 必備知識
Earned Oct 14, 2019 EDT
Google Cloud 必備知識
Earned Oct 7, 2019 EDT
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「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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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 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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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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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 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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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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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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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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Workspace 是 Google 的協作應用程式 平台，由 Google Cloud 提供。在這堂入門課程中，您將 從使用者角度實作練習 Workspace 的核心應用程式。雖然 Workspace 包含的應用程式和工具元件遠比本課程介紹的還要多， 您將體驗的是主要應用程式，包括 Gmail、日曆、試算表以及 其他幾項。每個實驗室功能需要 10 至 15 分鐘來完成，但您也可以視需要花更多時間 自行探索應用程式。
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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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