使用資料庫遷移服務將 MySQL 資料遷移至 Cloud SQL
Earned Oct 26, 2023 EDT
Enterprise Database Migration
Earned Oct 13, 2023 EDT
建立及管理 AlloyDB 執行個體
Earned Sep 29, 2023 EDT
建立及管理 Bigtable 執行個體
Earned Sep 28, 2023 EDT
建立及管理 Cloud Spanner 執行個體
Earned Sep 26, 2023 EDT
Google Cloud 基礎知識：核心基礎架構
Earned May 26, 2023 EDT
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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In this course you will get hands-on in order to work through real-world challenges faced when building streaming data pipelines. The primary focus is on managing continuous, unbounded data with Google Cloud products.
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In this intermediate course, you will learn to design, build, and optimize robust batch data pipelines on Google Cloud. Moving beyond fundamental data handling, you will explore large-scale data transformations and efficient workflow orchestration, essential for timely business intelligence and critical reporting.
Get hands-on practice using Dataflow for Apache Beam and Serverless for Apache Spark (Dataproc Serverless) for implementation, and tackle crucial considerations for data quality, monitoring, and alerting to ensure pipeline reliability and operational excellence. A basic knowledge of data warehousing, ETL/ELT, SQL, Python, and Google Cloud concepts is recommended.
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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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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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完成「Introduction to Generative AI」、「Introduction to Large Language Models」和「Introduction to Responsible AI」課程，即可獲得技能徽章。通過最終測驗，就能展現您對生成式 AI 基本概念的掌握程度。
「技能徽章」是 Google Cloud 核發的數位徽章，用於表彰您對 Google Cloud 產品和服務的相關知識。您可以將技能徽章公布在社群媒體的個人資料中，向其他人分享您的成果。
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這個入門微學習課程主要介紹「負責任的 AI 技術」和其重要性，以及 Google 如何在自家產品中導入這項技術。本課程也會說明 Google 的 7 個 AI 開發原則。
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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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這個入門微學習課程主要說明生成式 AI 的定義和使用方式，以及此 AI 與傳統機器學習方法的差異。本課程也會介紹各項 Google 工具，協助您開發自己的生成式 AI 應用程式。
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The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code. You will understand the benefits of Big Query ML. We then discuss how to optimize a machine learning (ML) model and how generalization and sampling can help assess the quality of ML models for custom training.
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本課程介紹 Google Cloud 的 AI 和機器學習 (ML) 功能，著重說明如何開發生成式和預測式 AI 專案。我們也會探討「從資料到 AI」整個生命週期都適用的技術、產品和工具，並透過互動式練習，協助資料科學家、AI 開發人員和機器學習工程師精進專業知識。
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This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.
完成 使用資料庫遷移服務將 MySQL 資料遷移至 Cloud SQL 技能徽章入門課程，證明您具備下列技能： 使用「資料庫遷移服務」中各種可用的工作類型和連線選項， 將 MySQL 資料遷移至 Cloud SQL，以及在執行「資料庫遷移服務」工作時 遷移 MySQL 使用者資料。
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This course is intended to give architects, engineers, and developers the skills required to help enterprise customers architect, plan, execute, and test database migration projects. Through a combination of presentations, demos, and hands-on labs participants move databases to Google Cloud while taking advantage of various services.
This course covers how to move on-premises, enterprise databases like SQL Server to Google Cloud (Compute Engine and Cloud SQL) and Oracle to Google Cloud bare metal.
Serverless Data Processing with Dataflow: Foundations
Earned Apr 9, 2025 EDT
Build Streaming Data Pipelines on Google Cloud
Earned Apr 9, 2025 EDT
Build Batch Data Pipelines on Google Cloud
Earned Apr 7, 2025 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned Apr 1, 2025 EDT
負責任的 AI 技術：透過 Google Cloud 採用 AI 開發原則
Earned Mar 11, 2024 EDT
Generative AI Fundamentals - 繁體中文
Earned Mar 10, 2024 EDT
負責任的 AI 技術簡介
Earned Mar 7, 2024 EST
大型語言模型簡介
Earned Mar 7, 2024 EST
生成式 AI 簡介
Earned Mar 7, 2024 EST
Launching into Machine Learning
Earned Jan 23, 2024 EST
Google Cloud 的 AI 和機器學習服務簡介
Earned Dec 16, 2023 EST
Google Cloud Big Data and Machine Learning Fundamentals
Earned Nov 2, 2023 EDT
建立及管理 PostgreSQL 適用的 Cloud SQL 執行個體
Earned Oct 30, 2023 EDT
使用資料庫遷移服務將 MySQL 資料遷移至 Cloud SQL
Earned Oct 26, 2023 EDT
Enterprise Database Migration
Earned Oct 13, 2023 EDT
建立及管理 AlloyDB 執行個體
Earned Sep 29, 2023 EDT
建立及管理 Bigtable 執行個體
Earned Sep 28, 2023 EDT
建立及管理 Cloud Spanner 執行個體
Earned Sep 26, 2023 EDT
Google Cloud 基礎知識：核心基礎架構
Earned May 26, 2023 EDT