按照您自己的方式探索 Google Cloud 培训。
Google Cloud 提供 980 多项学习活动供您选择，我们设计的目录完整全面，充分考虑了您的需求。该目录包含各种可供您选择的活动形式，既有简短的单个实验，也有由视频、文档、实验和测验组成的多模块课程，您可以根据需求进行选择。我们的实验可为您提供实际云资源的临时凭据，以便您通过实际操作掌握 Google Cloud 知识。您可以跟踪、衡量和了解自己的 Google Cloud 学习进度，完成学习活动即可赢取徽章！
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实验 精选 Analyze Speech & Language with Google APIs: Challenge Lab
This challenge lab tests your skills and knowledge from the labs in the Analyze Speech & Language with Google APIs quest. You should be familiar with the content of labs before attempting this lab.
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实验 精选 创建虚拟机 - Creating a Virtual Machine
在此实操实验中，您将学习如何创建 Google Compute Engine 虚拟机，并了解可用区、区域和不同的机器类型。您可以观看下面的短视频，先行了解一下实验内容：GCP 基础知识 - 创建虚拟机。
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实验 精选 适用于 BigQuery 和 Cloud SQL 的 SQL 简介
在本实验中，您将学习 SQL 子句的基础知识，并开展在 BigQuery 和 Cloud SQL 上运行结构化查询的实操练习。
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实验 精选 Configure Managed Guest Sessions for Public Access or Shared ChromeOS Devices
In this lab you'll set up ChromeOS Managed Guest Sessions settings.
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实验 精选 Migrating On-premises MySQL Using a Continuous Database Migration Service Job
In this lab, you migrate an on-premises MySQL database (running on a virtual machine) to Cloud SQL for MySQL using a continuous Database Migration Service job and VPC peering for connectivity. After you create and run the migration job, you confirm that an initial copy of your database has been successfully migrat…
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实验 精选 Connect an App to a Cloud SQL for PostgreSQL Instance
In this lab, you create a Kubernetes cluster and deploy a simple application to that cluster. Then, you connect the application to the supplied Cloud SQL for PostgreSQL database instance and confirm that it is able to write to and read from it.
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实验 精选 Identify different batch and streaming data sources
Differentiate between batch and streaming data sources, formats, and frequency
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实验 精选 Optical Character Recognition (OCR) with Document AI (Python)
In this lab, you will learn how to perform Optical Character Recognition using the Document AI API with Python.
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实验 精选 Vertex AI：Qwik Start
在本实验中，您将使用 BigQuery 进行数据处理和探索性数据分析，并使用 Vertex AI 平台训练和部署自定义 TensorFlow 回归器模型，以预测客户生命周期价值 (CLV)。实验的目标是通过高价值的实际应用场景“可预测 CLV”来介绍 Vertex AI。从 BigQuery 和 TensorFlow 工作流开始，逐步进展到使用 Vertex AI 在云端训练和部署模型。