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Cameron Goodhue

成为会员时间:2023

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
Responsible AI: 和 Google Cloud 一起践行 AI 原则 Earned Mar 11, 2024 EDT
Generative AI Fundamentals - 简体中文 Earned Mar 10, 2024 EDT
负责任的 AI 简介 Earned Mar 7, 2024 EST
大型语言模型简介 Earned Mar 7, 2024 EST
生成式 AI 简介 Earned Mar 7, 2024 EST
Launching into Machine Learning Earned Jan 23, 2024 EST
Google Cloud 上的 AI 和机器学习简介 Earned Dec 16, 2023 EST
Google Cloud Big Data and Machine Learning Fundamentals Earned Nov 2, 2023 EDT
创建和管理 Cloud SQL for PostgreSQL 实例 Earned Oct 30, 2023 EDT
用 Database Migration Service 将 MySQL 数据迁移至 Cloud SQL Earned Oct 26, 2023 EDT
Enterprise Database Migration Earned Oct 13, 2023 EDT
创建和管理 AlloyDB 实例 Earned Sep 29, 2023 EDT
创建和管理 Bigtable 实例 Earned Sep 28, 2023 EDT
创建和管理 Cloud Spanner 实例 Earned Sep 26, 2023 EDT
Google Cloud 基础知识:核心基础设施 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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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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完成 Introduction to Generative AI、Introduction to Large Language Models 和 Introduction to Responsible AI 三门课程,赢取技能徽章。通过最终测验,即表明您理解了生成式 AI 的基本概念。 技能徽章是由 Google Cloud 颁发的数字徽章,旨在认可您对 Google Cloud 产品与服务的了解程度。公开您的个人资料并将技能徽章添加到您的社交媒体个人资料中,以此来分享您获得的成就。

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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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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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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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本课程介绍 Google Cloud 的 AI 和机器学习 (ML) 能力,重点讲解如何开发生成式和预测式 AI 项目。本课程将探讨“数据到 AI”全生命周期中的多种技术、产品和工具,并通过互动练习帮助数据科学家、AI 开发者和机器学习工程师提升专业能力。

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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.

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完成“创建和管理 Cloud SQL for PostgreSQL 实例”这一入门级的技能徽章课程,展示您在以下方面的技能: 迁移、配置和管理 Cloud SQL for PostgreSQL 实例及数据库。

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完成用 Database Migration Service 将 MySQL 数据迁移至 Cloud SQL 这一入门级的技能徽章课程,展示您在以下方面的技能: 使用 Database Migration Service 中提供的不同作业类型和连接选项,将 MySQL 数据迁移到 Cloud SQL; 以及在运行 Database Migration Service 作业时 迁移 MySQL 用户数据。

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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.

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完成入门级技能徽章课程创建和管理 AlloyDB 实例,展示您在以下方面的技能:执行核心 AlloyDB 操作 和任务、从 PostgreSQL 迁移到 AlloyDB、管理 AlloyDB 数据库,以及 使用 AlloyDB 列式引擎加速分析查询。

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完成入门级技能徽章课程创建和管理 Bigtable 实例,展示以下方面的技能:创建实例、设计架构、 查询数据,以及在 Bigtable 中执行管理任务,包括监控性能、配置节点自动扩缩和复制。

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完成创建和管理 Cloud Spanner 实例 这一入门级技能徽章课程,展示您在以下方面的技能: 创建 Cloud Spanner 实例和数据库并与之互动; 使用各种方法加载 Cloud Spanner 数据库; 备份 Cloud Spanner 数据库;定义架构并了解查询计划; 部署连接到 Cloud Spanner 实例的现代 Web 应用。

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“Google Cloud 基础知识:核心基础设施”介绍在使用 Google Cloud 时会遇到的重要概念和术语。本课程通过视频和实操实验来介绍并比较 Google Cloud 的多种计算和存储服务,并提供重要的资源和政策管理工具。

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