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在 Google Cloud 控制台中运用您的技能

George Balan

成为会员时间：2020

白银联赛

13444 积分
Responsible AI: 和 Google Cloud 一起践行 AI 原则 Earned Oct 8, 2025 EDT
Data Warehousing for Partners: Process Data with Dataflow Earned Jun 15, 2025 EDT
Data Warehousing for Partners: Analyze Data with Looker Earned Jun 15, 2025 EDT
Data Warehousing for Partners: Process Data with Dataproc Earned Jun 15, 2025 EDT
Data Warehousing for Partners: Optimize in BigQuery Earned Jun 12, 2025 EDT
Data Warehousing for Partners: Stream Data with Pub/Sub Earned Jun 12, 2025 EDT
Data Warehousing for Partners: Cloud Data Fusion Pipelines Earned Jun 12, 2025 EDT
Data Warehousing for Partners: Migrate Data to BigQuery Earned Jun 11, 2025 EDT
Data Warehousing for Partners: Design in BigQuery Earned Jun 10, 2025 EDT
Data Warehousing for Partners: Enable Google Cloud Customers Earned Apr 14, 2025 EDT
负责任的 AI 简介 Earned Nov 27, 2023 EST
Generative AI Fundamentals - 简体中文 Earned Nov 24, 2023 EST
大型语言模型简介 Earned Nov 24, 2023 EST
生成式 AI 简介 Earned Nov 22, 2023 EST
使用 BigQuery 构建数据仓库 Earned Aug 23, 2023 EDT
在 Google Cloud 上为机器学习 API 准备数据 Earned Jul 14, 2023 EDT
Serverless Data Processing with Dataflow: Foundations Earned Jun 23, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud Earned Jun 20, 2023 EDT
Build Streaming Data Pipelines on Google Cloud Earned Jun 15, 2023 EDT
Build Batch Data Pipelines on Google Cloud Earned Jun 11, 2023 EDT
Build Data Lakes and Data Warehouses on Google Cloud Earned Jun 3, 2023 EDT
Intro to BigQuery: Analytics & Machine Learning Earned Dec 14, 2021 EST
BigQuery for Data Analysis I Earned Dec 8, 2021 EST

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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This course continues to explore the implementation of data load and transformation pipelines for a BigQuery Data Warehouse using Dataflow.

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This course explores how to leverage Looker to create data experiences and gain insights with modern business intelligence (BI) and reporting.

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This course explores the implementation of data load and transformation pipelines for a BigQuery Data Warehouse using Dataproc.

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Welcome to Optimize in BigQuery, where we map Enterprise Data Warehouse concepts and components to BigQuery and Google data services with a focus on optimization.

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This course explores how to implement a streaming analytics solution using Pub/Sub.

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This course continues to explore the implementation of data load and transformation pipelines for a BigQuery Data Warehouse using Cloud Data Fusion.

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This course identifies best practices for migrating data warehouses to BigQuery and the key skills required to perform successful migration.

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Welcome to Design in BigQuery, where we map Enterprise Data Warehouse concepts and components to BigQuery and Google data services with a focus on schema design.

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This course discusses the key elements of Google's Data Warehouse solution portfolio and strategy.

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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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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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完成中级技能徽章课程使用 BigQuery 构建数据仓库，展示以下技能： 联接数据以创建新表、排查联接故障、使用并集附加数据、创建日期分区表， 以及在 BigQuery 中使用 JSON、数组和结构体。 技能徽章是 Google Cloud 颁发的专属数字徽章， 旨在认可您在 Google Cloud 产品与服务方面的熟练度； 您需要在交互式实操环境中参加考核，证明自己运用所学知识的能力后 才能获得。完成此技能徽章课程和作为最终评估的实验室挑战赛， 获得数字徽章，在您的人际圈中炫出自己的技能。

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完成入门级技能徽章课程在 Google Cloud 上为机器学习 API 准备数据，展示以下技能： 使用 Dataprep by Trifacta 清理数据、在 Dataflow 中运行数据流水线、在 Dataproc 中创建集群和运行 Apache Spark 作业，以及调用机器学习 API，包括 Cloud Natural Language API、Google Cloud Speech-to-Text API 和 Video Intelligence API。

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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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Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.

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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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Welcome Gamers! Today's game is all about experimenting with Big Query for Machine Learning! Use real life case studies to learn various concepts of BQML and have fun. Take labs to earn points. The faster you complete the lab objectives, the higher your score.

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Welcome Gamers! Learn BigQuery and Cloud SQL, all while having fun! You will compete to see who can finish the game with the highest score. Earn the points by completing the steps in the lab.... and get bonus points for speed! Be sure to click "End" when you're done with each lab to get the maximum points. All players will be awarded the game badge.

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