On-demand activities
Find the right on-demand learning activities for you. Labs are short learning activities that teach you a specific lesson by giving you direct, temporary, hands-on access to real cloud resources. Courses are longer activities, consisting of several modules made of videos, documents, hands-on labs and quizzes. Finally, quests are similar, but are usually shorter and contain only labs.
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Lab Featured Build an application to send Chat Prompts using the Gemini model
In this lab, you will learn how to use Google's Vertex AI SDK to interact with the powerful Gemini generative AI model, enabling you to send text based chat prompts as an input and receive personalized streaming and non-streaming chat responses.
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Lab Featured Creating and Populating a Bigtable Instance
In this lab, you create a Bigtable instance and table and then use a Dataflow template to populate the table from pre-generated data files on Cloud Storage.
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Lab Featured Getting Started with the Vertex AI Gemini API and Python SDK
In this lab, you will learn how to use the Vertex AI SDK for Python to call the Vertex AI Gemini API.
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Lab Featured Deploy a Compute Instance with a Remote Startup Script: Challenge Lab
Configure a Linux Google Compute Engine instance that installs an Apache web server software using a remote startup script, then confirm that Apache has successfully installed.
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Lab Featured Using Custom Fields in Looker Explores
In this lab, you will learn how to utilize custome fields in Looker Explores queries.
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Lab Featured Gating Deployments with Binary Authorization
In this lab you will learn about the tools and techniques to secure deployed artifacts.
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Lab Featured Caching and Datagroups with LookML
In this lab, you learn how caching works in Looker and explore how to use LookML objects called datagroups to define caching policies.
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Lab Featured Managing Environments with Dialogflow CX
In this lab, you'll explore Managing Environments with Dialogflow.
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Lab Featured Deploying a Multi-Cluster Gateway Across GKE Clusters
Learn how to enable, use, and deploy the multi-cluster Google Kubernetes Engine (GKE) Gateway controller