Google Cloud Next 2025

In this lab, you learn how to perform multimodal retrieval augmented generation (RAG) using Gemini API in Vertex AI.

In this lab, you will create and train a Custom Document Extractor that processes W-2 (US tax form) documents.

In this lab, you learn to build generative AI prototypes with Vertex AI Studio and Gemini, mastering prompt design and multimodal analysis in the console, all without coding.

In this lab, you will learn how to use the Gemini API in Vertex AI and the Google Gen AI SDK for Python with the Gemini 2.0 Flash model.

In this lab, you will learn how to generate and execute code using the Gemini API in Vertex AI and the Google Gen AI SDK for Python with the Gemini 2.0 Flash model.

In this lab, you will explore the Vertex AI Embeddings API for both Text and Multimodal (Images and Video) use cases.

In this lab, you build and deploy an AI-based chat application on Cloud Run that utilizes a Gemini model.

In this lab, you will learn how to build a question answering system that understands both text and images with Vertex AI.

In this lab, you will learn how to deploy a Streamlit app integrated with Gemini on Cloud Run.

Learn how to use BigQuery Machine Learning with remote models (Gemini) to analyze customer reviews using SQL.

In this lab you learn how to use Gemini code generation, explanation and suggestions in BigQuery.

In this lab you will learn how to use Table Explorer and Data Insights with Gemini in BigQuery.

In this lab, you learn how to create a RAG pipeline from embeddings, vector search, to generation with Gemini in BigQuery.

In this lab, you will learn how to build a BI dashboard with Looker Studio as the front end, powered by BigQuery on the back end.

In this lab, you import data from CSV text files into Cloud SQL and then carry out some basic data analysis using simple queries.

In this lab, you perform basic administrative tasks within a Cloud Spanner instance.

In this lab, you migrate a stand-alone PostgreSQL database (running on a virtual machine) to AlloyDB for PostgreSQL using a continuous Database Migration Service job with VPC peering for connectivity.

This lab teaches you how logging works on GKE and some best practices for log collection by going through some common logging use cases.

Create a delivery pipeline using Google Cloud Deploy, create a release for a basic application, and promote the application through a series of Google Kubernetes Engine targets

Use Log Analytics for in-depth usage logs analysis through SQL querires from the Cloud Logging page.

In this lab you learn how to create a secure, high-throughput VPN and test the speed.

In this lab, you learn how to provision a complete Kubernetes cluster using Google Kubernetes Engine, deploy and manage Docker containers using kubectl, and break an application into microservices using Kubernetes' Deployments and Services.

This lab demonstrates how to create an HTTP(S) load balanced that forwards traffic to instances in two different regions.

In this hands-on lab you will build, change, provision, and destroy infrastructure using Terraform in the cloud environment.

Learn how to set up a network, add 3 subnetworks, then apply firewall rules.

In this lab you create an auto-mode VPC network with firewall rules and 2 VM instances, then explore the connectivity for the VM instances.

In this lab, you build several data pipelines that ingest and transform data from a publicly available dataset into BigQuery.

In this lab you build a Google Workspace add-on that uses Gemini and Vertex AI to analyze Gmail sentiment, automatically labeling negative emails for prioritized handling.