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 Getting started with Firebase Web
In this hands-on lab, you will learn about the Firebase product suite with Web.
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实验 精选 Migrating an application and data from Apache Cassandra™ to DataStax Enterprise
In this lab, you will learn how to migrate an application running on Apache Cassandra™ to DataStax Enterprise (DSE). To do this, you will deploy a Cassandra™ database and an application that writes data into it. You will then deploy a DataStax Enterprise database and connect the same application to the database. F…
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实验 精选 Scaling VM-Series to Secure Google Cloud Networks
Secure Google Cloud hub-and-spoke topology with VM-Series at scale.
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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。
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实验 精选 Cloud Armor Preconfigured WAF Rules
Mitigate some common vulnerabilities by using Google Cloud Armor WAF rules.
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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.
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实验 精选 Build an LLM and RAG-based Chat Application with AlloyDB and Vertex AI
In this lab, you create a chat application that uses Retrieval Augmented Generation, or RAG, to augment prompts with data retrieved from AlloyDB.
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实验 精选 在 Google Cloud 上为机器学习 API 准备数据：实验室挑战赛
此实验室挑战赛旨在检验您通过“在 Google Cloud 上为机器学习 API 准备数据”课程的各个实验所掌握的技能和知识。在尝试此挑战赛之前，您应该先熟悉各个实验的内容。
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实验 精选 Distributed Image Processing in Cloud Dataproc
In this lab, you will learn how to use Apache Spark on Cloud Dataproc to distribute a computationally intensive image processing task onto a cluster of machines.
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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.