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 Images with the Cloud Vision API: Challenge Lab
This challenge lab tests your skills and knowledge from the labs in the Analyze Images with the Cloud Vision API quest. You should be familiar with the content of labs before attempting this lab.
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实验 精选 Speech to Text Transcription with the Cloud Speech API
The Cloud Speech API lets you do speech to text transcription from audio files in over 80 languages. In this hands-on lab you’ll record your own audio file and send it to the Speech API for transcription.
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实验 精选 Create a Custom Network and Apply Firewall Rules
Use the gcloud command line to set up a VPN and 3 subnetworks, then apply firewalls.
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实验 精选 Generative AI: Cloud Architect Revision
Arcade chatbot lab to learn about the Professional Cloud Architect
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实验 精选 IAM 自定义角色
Cloud IAM 提供了适当的工具来管理资源权限，操作简单，自动化程度高。您不能直接授予用户权限。不过，您可以向其授予角色，角色捆绑了一项或多项权限。这样，您就可以将公司中的工作职责映射至多个群组和角色。
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实验 精选 Form Parsing with Document AI (Python)
In this lab, you will learn how to use the Document AI Form Parser to parse a handwritten form with Python.
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实验 精选 Dialogflow CX: Managing Environments
In this lab you will continue using a virtual agent and learn how to create versions, environments, and test your bot
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实验 精选 Speech-to-Text API：Qwik Start
Speech-to-Text API 可用于将语音识别技术集成到开发者应用中，让您在发送音频后能收到转写成的文字。欢迎观看下面两个简短视频：基于 Google 机器学习的强大语音识别技术和 Google Cloud Speech：Qwik Start - Qwiklabs 预览。
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实验 精选 Visualize the 10,000 Bitcoin Pizza Transaction Using BigQuery and Vertex AI Workbench
In this lab you will use an Vertex AI Notebook instance to retrieve as many transactions as possible from BigQuery within 2 degrees of separation from the pizza exchange, post-process the transactions to remove excess transaction, then visualize the directed graph.
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实验 精选 Monitoring and Managing Bigtable Health and Performance
In this lab, you monitor disk and CPU usage in a Bigtable instance, update an existing cluster to apply node autoscaling, implement replication in an instance, and back up and restore data in Bigtable.