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Mario Perdichizzi

成为会员时间：2020

青铜联赛

1260 积分
Google Cloud 重要基础设施：核心服务 Earned Jun 7, 2022 EDT
Google Cloud 基础知识：核心基础设施 Earned May 31, 2022 EDT
为 Compute Engine 实现云负载均衡 Earned Apr 17, 2021 EDT
Google Cloud 基础知识 Earned Oct 30, 2020 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud Earned Oct 30, 2020 EDT
Build Streaming Data Pipelines on Google Cloud Earned Sep 11, 2020 EDT
Build Batch Data Pipelines on Google Cloud Earned Aug 24, 2020 EDT
Build Data Lakes and Data Warehouses on Google Cloud Earned Aug 10, 2020 EDT
[DEPRECATED] Google Cloud Big Data and Machine Learning Fundamentals Earned Aug 9, 2020 EDT

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，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、系统和应用服务等基础架构组件。本课程的内容还包括如何部署实用的解决方案，包括客户提供的加密密钥、安全和访问权限管理、配额和结算，以及资源监控。

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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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完成入门级技能徽章课程为 Compute Engine 实现云负载均衡，展示以下方面的技能： 在 Compute Engine 中创建和部署虚拟机 以及配置网络和应用负载均衡器。

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在本入门级课程中，您将了解 Google Cloud 的基础工具和服务。此课程提供了可选视频， 旨在帮助您深入了解和回顾实验中涉及的概念。Google Cloud 基础知识是推荐给 Google Cloud 学员的第一门课程 - 即使您几乎没有云相关知识，也能从中获得实践 经验，并将其直接运用于您的首个 Google Cloud 项目。从编写 Cloud Shell 命令和部署您的第一个虚拟机，到在 Kubernetes Engine 上运行应用 或者使用负载均衡，“Google Cloud 基础知识”都是您了解该平台 基本功能的首选入门级课程。

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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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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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