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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1203 条结果
  1. 实验 精选

    Create Text Embeddings for a Vector Store using LangChain

    In this lab, you learn how to use LangChain to store documents as embeddings in a vector store. You will use the LangChain framework to split a set of documents into chunks, vectorize (embed) each chunk and then store the embeddings in a vector database.

  2. 实验 精选

    Getting started with Firebase Web

    In this hands-on lab, you will learn about the Firebase product suite with Web.

  3. 实验 精选

    Getting Started with BigQuery GIS for Data Analysts

    BigQuery GIS allows you to easily analyze and visualize geospatial data in BigQuery.

  4. 实验 精选

    Prepare Data for Looker Dashboards and Reports:挑戰實驗室

    在本挑戰實驗室中,您將測試自身篩選、透視、計算及合併資料的技能,建構 Looker 資訊主頁和報表。

  5. 实验 精选

    Classify Images with TensorFlow on Google Cloud: Challenge Lab

    This challenge lab tests your skills and knowledge from the labs in the Classify Images with TensorFlow on Google Cloud course. You should be familiar with the content of these labs before attempting this lab.

  6. 实验 精选

    Scanning User-generated Content Using the Cloud Video Intelligence and Cloud Vision APIs

    This lab will show you how to deploy a set of Cloud Functions in order to process images and videos with the Cloud Vision API and Cloud Video Intelligence API.

  7. 实验 精选

    Create a report in Looker Studio

    Use Looker Studio to build a report.

  8. 实验 精选

    使用 Firebase 建構無伺服器網頁應用程式

    在本研究室中,您將使用 Firebase 建立無伺服器網頁應用程式,供使用者上傳資訊並向虛構的 Pet Theory 診所預約看診時間。

  9. 实验 精选

    Vertex AI: Training and Serving a Custom Model

    In this lab, you will use Vertex AI to train and serve a TensorFlow model using code in a custom container.

  10. 实验 精选

    Set up a SIEM forwarder on a Linux Instance

    Install and configure a SIEM forwarder on a Linux host, then send sample logs using a file collector and observe the ingested logs in a live Chronicle environment.