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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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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这是一套自助式速成课程，向学员介绍 Google Cloud 提供的灵活全面的基础架构和平台服务。学员将通过一系列视频讲座、演示和实操实验，探索和部署各种解决方案元素，包括安全互连网络、负载均衡、自动扩缩、基础架构自动化和代管式服务。
利用 BigQuery ML 构建预测模型时的数据工程处理
Earned Oct 24, 2023 EDT
使用 BigQuery 构建数据仓库
Earned Oct 22, 2023 EDT
在 Google Cloud 上为机器学习 API 准备数据
Earned Oct 20, 2023 EDT
Serverless Data Processing with Dataflow: Foundations
Earned Oct 7, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Oct 6, 2023 EDT
Build Streaming Data Pipelines on Google Cloud
Earned Oct 5, 2023 EDT
Build Batch Data Pipelines on Google Cloud
Earned Sep 30, 2023 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned Sep 27, 2023 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned Sep 14, 2023 EDT
Google Cloud 弹性基础设施：扩缩和自动化
Earned Jun 21, 2023 EDT
开发 Google Cloud 网络
Earned Jun 14, 2023 EDT
Google Cloud 重要基础设施：核心服务
Earned Jun 13, 2023 EDT
Google Cloud 重要基础设施：基础
Earned May 31, 2023 EDT
在 Google Cloud 上设置应用开发环境
Earned May 28, 2023 EDT
为 Compute Engine 实现云负载均衡
Earned May 26, 2023 EDT