利用 BigQuery ML 构建预测模型时的数据工程处理
Earned Jul 4, 2024 EDT
使用 Dataplex 构建数据网格
Earned Jun 19, 2024 EDT
使用 BigQuery 构建数据仓库
Earned Jun 5, 2024 EDT
在 Google Cloud 上为机器学习 API 准备数据
Earned Jun 3, 2024 EDT
Serverless Data Processing with Dataflow: Foundations
Earned May 22, 2024 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned May 18, 2024 EDT
Building Resilient Streaming Systems on Google Cloud Platform
Earned May 13, 2024 EDT
Build Batch Data Pipelines on Google Cloud
Earned Apr 27, 2024 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned Apr 13, 2024 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned Mar 31, 2024 EDT
Google Cloud 重要基础设施：基础
Earned Aug 17, 2022 EDT
在 Google Cloud 上设置应用开发环境
Earned Aug 12, 2022 EDT
在 Compute Engine 上实现负载均衡
Earned Aug 10, 2022 EDT
Google Cloud Essentials
Earned Aug 9, 2022 EDT
Google Cloud 基础知识：核心基础设施
Earned Aug 4, 2022 EDT
完成中级技能徽章课程利用 BigQuery ML 构建预测模型时的数据工程处理， 展示自己在以下方面的技能：利用 Dataprep by Trifacta 构建 BigQuery 数据转换流水线； 利用 Cloud Storage、Dataflow 和 BigQuery 构建提取、转换和加载 (ETL) 工作流； 以及利用 BigQuery ML 构建机器学习模型。
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完成入门技能徽章课程使用 Dataplex 构建数据网格，展示以下方面的技能：使用 Dataplex 构建数据网格， 以在 Google Cloud 上实现数据安全、治理和发现。您将在 Dataplex 中练习和测试自己在标记资产、分配 IAM 角色和评估数据质量方面的技能。
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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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This 1-week, accelerated on-demand course builds upon Google Cloud Platform Big Data and Machine Learning Fundamentals. Through a combination of video lectures, demonstrations, and hands-on labs, you'll learn to build streaming data pipelines using Google cloud Pub/Sub and Dataflow to enable real-time decision making. You will also learn how to build dashboards to render tailored output for various stakeholder audiences.
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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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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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这门自助式速成课程向学员介绍 Google Cloud 提供的灵活全面的基础架构和平台服务，其中着重介绍了 Compute Engine。学员将通过一系列视频讲座、演示和动手实验，探索和部署各种解决方案元素，包括网络、虚拟机和应用服务等基础架构组件。您将学习如何通过控制台和 Cloud Shell 使用 Google Cloud。您还将了解云架构师角色、基础架构设计方法以及虚拟网络配置和虚拟私有云 (VPC)、项目、网络、子网、IP 地址、路由及防火墙规则。
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完成“在 Google Cloud 上设置应用开发环境”课程，赢取技能徽章；通过该课程，您将了解如何使用以下技术的基本功能来构建和连接以存储为中心的云基础设施： Cloud Storage、Identity and Access Management、Cloud Functions 和 Pub/Sub。
利用 BigQuery ML 构建预测模型时的数据工程处理
Earned Jul 4, 2024 EDT
使用 Dataplex 构建数据网格
Earned Jun 19, 2024 EDT
使用 BigQuery 构建数据仓库
Earned Jun 5, 2024 EDT
在 Google Cloud 上为机器学习 API 准备数据
Earned Jun 3, 2024 EDT
Serverless Data Processing with Dataflow: Foundations
Earned May 22, 2024 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned May 18, 2024 EDT
Building Resilient Streaming Systems on Google Cloud Platform
Earned May 13, 2024 EDT
Build Batch Data Pipelines on Google Cloud
Earned Apr 27, 2024 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned Apr 13, 2024 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned Mar 31, 2024 EDT
Google Cloud 重要基础设施：基础
Earned Aug 17, 2022 EDT
在 Google Cloud 上设置应用开发环境
Earned Aug 12, 2022 EDT
在 Compute Engine 上实现负载均衡
Earned Aug 10, 2022 EDT
Google Cloud Essentials
Earned Aug 9, 2022 EDT
Google Cloud 基础知识：核心基础设施
Earned Aug 4, 2022 EDT