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Qing Sun

成为会员时间:2024

钻石联赛

25825 积分
Google Cloud 基礎知識:核心基礎架構 Earned Jun 17, 2025 EDT
Google Cloud 的 AI 和機器學習服務簡介 Earned Jun 4, 2025 EDT
負責任的 AI 技術:透過 Google Cloud 採用 AI 開發原則 Earned Jun 3, 2025 EDT
Google Cloud 中的資料工程簡介 Earned Jun 3, 2025 EDT
Build Data Lakes and Data Warehouses on Google Cloud Earned May 18, 2025 EDT
Serverless Data Processing with Dataflow: Develop Pipelines Earned Jan 20, 2025 EST
Serverless Data Processing with Dataflow: Foundations Earned Jan 3, 2025 EST
Professional Machine Learning Engineer Study Guide Earned Dec 11, 2024 EST
Innovating with Google Cloud Artificial Intelligence Earned Dec 4, 2024 EST
Preparing for your Professional Data Engineer Journey Earned Dec 2, 2024 EST
Google Cloud 資料分析簡介 Earned Dec 2, 2024 EST
大型語言模型簡介 Earned Nov 26, 2024 EST

「Google Cloud 基礎知識:核心基礎架構」介紹了在使用 Google Cloud 時會遇到的重要概念和術語。本課程會透過影片和實作實驗室,介紹並比較 Google Cloud 的多種運算和儲存服務,同時提供重要的資源和政策管理工具。

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本課程介紹 Google Cloud 中的 AI 和機器學習 (ML) 服務。這些服務可建構預測式和生成式 AI 專案。我們將帶您探索「從資料到 AI」生命週期中適用的技術、產品和工具,包括 AI 基礎、開發選項及解決方案。課程目的是藉由生動的學習體驗與實作練習,增進數據資料學家、AI 開發人員和機器學習工程師的技能與知識。

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隨著企業持續擴大使用人工智慧和機器學習,以負責任的方式發展相關技術也日益重要。對許多企業來說,談論負責任的 AI 技術可能不難,如何付諸實行才是真正的挑戰。如要瞭解如何在機構中導入負責任的 AI 技術,本課程絕對能助您一臂之力。 您可以從中瞭解 Google Cloud 目前採取的策略、最佳做法和經驗談,協助貴機構奠定良好基礎,實踐負責任的 AI 技術。

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在本課程中,您會學到 Google Cloud 上的資料工程、資料工程師的角色與職責,以及這些內容如何對應至 Google Cloud 提供的服務。您也將瞭解處理資料工程難題的許多方法。

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While the traditional approaches of using data lakes and data warehouses can be effective, they have shortcomings, particularly in large enterprise environments. This course introduces the concept of a data lakehouse and the Google Cloud products used to create one. A lakehouse architecture uses open-standard data sources and combines the best features of data lakes and data warehouses, which addresses many of their shortcomings.

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In this second installment of the Dataflow course series, we are going to be diving deeper on developing pipelines using the Beam SDK. We start with a review of Apache Beam concepts. Next, we discuss processing streaming data using windows, watermarks and triggers. We then cover options for sources and sinks in your pipelines, schemas to express your structured data, and how to do stateful transformations using State and Timer APIs. We move onto reviewing best practices that help maximize your pipeline performance. Towards the end of the course, we introduce SQL and Dataframes to represent your business logic in Beam and how to iteratively develop pipelines using Beam notebooks.

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This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next, we talk about the Apache Beam vision and the benefits of the Beam Portability framework. The Beam Portability framework achieves the vision that a developer can use their favorite programming language with their preferred execution backend. We then show you how Dataflow allows you to separate compute and storage while saving money, and how identity, access, and management tools interact with your Dataflow pipelines. Lastly, we look at how to implement the right security model for your use case on Dataflow.

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This course helps learners create a study plan for the PMLE (Professional Machine Learning Engineer) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.

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Artificial intelligence (AI) and machine learning (ML) represent an important evolution in information technologies that are quickly transforming a wide range of industries. “Innovating with Google Cloud Artificial Intelligence” explores how organizations can use AI and ML to transform their business processes. Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.

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This course helps learners create a study plan for the PDE (Professional Data Engineer) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.

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這堂初級課程將介紹 Google Cloud 的資料分析工作流程,以及用於探索、分析資料並以圖表呈現的工具。您也能學會如何與相關人員分享自己的發現結果。本課程包含個案研究、實作實驗室、講座、測驗和示範,實際展示如何將原始資料集轉化為清晰的資料,進而呈現出能發揮成效的圖表和資訊主頁。無論您是資料領域從業人員、想瞭解如何透過 Google Cloud 取得成功,或有意在職涯中更上一層樓,本課程都能協助您踏出第一步。絕大多數在工作上執行或運用資料分析的學員,都能從本課程受益。

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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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