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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  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

    为 Looker 信息中心和报告准备数据:实验室挑战赛

    在本实验室挑战赛中,您将检验自己过滤、透视、计算和合并数据以构建 Looker 信息中心和报告的技能。

  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 构建无服务器 Web 应用

    在本实验中,您将使用 Firebase 创建一个无服务器 Web 应用,该应用允许用户上传信息并与虚构的 Pet Theory 诊所预约。

  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.