On-demand activities
Google Cloud 根據您的需求規劃了全方位的課程內容，內含超過 980 項學習活動，並涵蓋多種活動型態，您可自由選擇。您可以選擇簡短的個別研究室，或是包含影片、文件、研究室和測驗的多單元課程。在研究室中，您可以透過臨時憑證實際使用雲端資源，直接累積 Google Cloud 實作經驗。完成課程可獲得徽章，讓您輕鬆掌握、追蹤及評估自己的 Google Cloud 學習成果！
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实验 精选 Secure Software Supply Chain: Create and Use Cloud Workstations
In this lab, you will use Cloud Workstations to create a workstation configuration and launch a new workstation instance from that configuration.
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实验 精选 Uptraining with Document AI Workbench
In this lab, you will will create an Invoice Parser processor, configure the processor for uptraining, label example documents, and uptrain the processor.
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实验 精选 Dialogflow CX: Parameter Manipulation
In this lab you will use regular expressions to do parameter validation (e.g., on a PNR Number) and reset parameters to null when the user starts a new flow of conversation.
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实验 精选 Online Data Migration to Cloud Spanner using Striim
In this lab you will learn how to migrate a Cloud SQL for MySQL database to Cloud Spanner using Google Cloud's data migration partner, Striim.
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实验 精选 Building Batch Pipelines in Cloud Data Fusion
This lab will teach you how to use the Pipeline Studio in Cloud Data Fusion to build an ETL pipeline. Pipeline Studio exposes the building blocks and built-in plugins for you to build your batch pipeline, one node at a time. You will also use the Wrangler plugin to build and apply transformations to your data that…
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实验 精选 Build and Configure an Integration using Application Integration
Learn the core concepts, functionalities, and best practices of Application Integration
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实验 精选 Manage a partitioned table in BigQuery
Manage a partitioned table and use filters to reduce data examined in BigQuery
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实验 精选 Set up a SIEM forwarder for Windows on Docker
In this lab, you configure a SIEM forwarder on a Windows VM using a standard Docker image. You use labels to add searchable metadata to the logs to optimize analytical capabilities.
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实验 精选 Machine Learning with Spark on Google Cloud Dataproc
In this lab you will learn how to implement logistic regression using a machine learning library for Apache Spark running on a Google Cloud Dataproc cluster to develop a model for data from a multivariable dataset