In this codelab, you’ll use Gemini Code Assist, an AI-powered collaborator in Google Cloud. You’ll get familiar with how you can use Gemini Chat and inline code assistance to generate code, understand code and other AI assisted coding tasks.

In this codelab you’ll learn how to deploy AlloyDB Omni on a compute VM, load data and use AlloyDB Columnar Engine to improve performance

Learn to access files in Cloud Storage with the Spring Resource abstraction.

In this codelab, you will learn how to set up advanced load balancing optimization features for global external application load balancing.

In this codelab you’ll learn how to deploy AlloyDB Omni on a GKE cluster, deploy an I model to the same cluster, register the model in the AlloyDB Omni and make them working together

In this codelab, we demonstrate a solution to access and analyze clinical data in GCP using BigQuery and AI Platform Notebooks.

In this codelab, you’ll learn about Cloud Profiler, a tool for continuous collection and analysis of the performance data from production environments with low overhead.

In this lab you’ll learn how to train an XGBoost model on a financial dataset, deploy it to Cloud AI Platform, and analyze it with the What-if Tool

This lab shows you how to set up Apache Spark and Jupyter Notebooks on Cloud Dataproc using Optional Components and Component Gateway.

In this codelab, you’ll learn how to use the DLP API to automatically classify data uploaded to Cloud Storage. Based on Automating the Classification of Data Uploaded to Cloud Storage

In this codelab, you’ll build a microservice that will battle other microservices by “throwing” jamón at each other in an arena.

Terms and Conditions for Battle Jamón

In this codelab, you’ll build a microservice that will battle other microservices by “throwing” leaves at each other in an arena.

In this codelab, you’ll build a microservice that will battle other microservices by “throwing” peaches at each other in an arena.

In this codelab, you will create a load balancer and associated backend service. You will then create a Cloud Armor bot management rule and learn how it protects your backends.

In this codelab, you will first learn how to create and use a regular Cloud Tasks queue for HTTP target tasks. Then, you’ll learn how to use queue-level HTTP URI override and the new BufferTask API to more easily buffer HTTP requests with Cloud Tasks.

In this codelab, you’ll build a knowledge-driven contextual Yoga Poses search app designed to answer user questions on Yoga poses to match with their questions. It also allows administrative tasks like creating and editing Yoga poses.

In this lab, you’ll directly ingest a BigQuery dataset and train a fraud detection model with TensorFlow Enterprise on Google Cloud AI Platform.

Learn to build a frontend Django client to create a conversational experience for a Dialogflow app.

In this codelab, you’ll build a Spring application using Kotlin and integrate with several Google Cloud Platform technologies, such as Cloud Pub/Sub and Cloud SQL.

In this codelab, you’ll build a knowledge-driven chat application designed to answer questions related to patent search and bring contextually relevant results grounded in the patent dataset truth as its source.

In this codelab, you’ll use Vertex AI to generate a trivia quiz according to some provided specifications. You’ll test your quiz generator in a cloud hosted developer environment, and then make it publicly available by deploying it to Google Cloud Run. At the end of the lab, you’ll integrate your quiz generator with a complete app.

In this codelab, you’ll learn how to build and run a Slack bot on Google Cloud. It uses Cloud Run, a fully managed compute platform that automatically scales your stateless containers.

In this codelab, you’ll build a knowledge-driven chat application designed to answer customer questions, guide product discovery, and tailor search results for an ecommerce dataset

In this codelab, you’ll build a knowledge-driven chat application designed to answer customer questions, guide product discovery, and tailor search results for an ecommerce dataset

Learn to create a simple chatbot by using Dialogflow.

In this lab, you’ll learn about how to train and deploy a forecasting model using AutoML in Vertex AI.

In this codelab, you will build an event-driven orchestration of microservices to process images with Eventarc and Workflows

Learn to build and launch a Spring Boot Java app from Cloud Shell without ever leaving your browser.

In this code lab, you’ll learn how to build and launch an ASP.NET Core app from Google Cloud Shell – without ever leaving the browser.

Out of the box Dialogflow comes with lots of integrations, for web, Google Assistant, social media and phone gateways. However, when you want to bring your chatbots to mobile devices, you will have to create a custom integration. This lab will teach you how to integrate Dialogflow Essentials into a Flutter app.

In this lab you’ll walk through a complete ML workflow on GCP: ingesting data from BigQuery, building an XGBoost model in a Cloud AI Platform Notebooks instance, and deploying the model to AI Platform.

In this lab you’ll learn how to train an XGBoost model on a financial dataset, deploy it to Vertex AI, and analyze it with the What-if Tool

In this codelab, you learn how to build a C# service to expose an API via gRPC and build a C# client to call your gRPC service.

In this codelab, you will learn how to build a Java-based service which exposes an API via gRPC. Then you will write a Java command-line client for your gRPC service.

In this codelab, you’ll gain hands-on experience with an architecture pattern commonly used to achieve scale and resiliency while handling real-time data. You will build an IoT device (Raspberry Pi) that measures weather data and then create a data pipeline including a message queue, serverless function, a Cloud-based data warehouse and an analytics dashboard using Google’s Cloud Platform.

In this codelab you’ll learn how to create an AlloyDB cluster, deploy GenAI Databases Retrieval Service for databases and create a sample application using the service.

In this codelab you’ll learn how to create a database, deploy GenAI Retrieval Service for databases and create a sample chat application using the service.

In this tutorial, you will learn how to train and run a model with vLLM on GKE through an Airflow DAG.

Learn to cache data from a Spring Boot app with Memorystore.

In this codelab, you’ll create a new Compute Engine instance, compile and run a program to calculate pi to millions of decimal places.

Learn how to create a Google Cloud project, and then call an API from within that project.

In this codelab, you will create a TCP/SSL proxy load balancer with a backend service and use Cloud Armor to limit access to the load balancer to only a specific set of user clients

In this codelab, you will create a TCP/SSL proxy load balancer with a backend service and use Cloud Armor to limit access to the load balancer to only a specific set of user clients

In this codelab, you will learn how to configure Cloud Armor Network edge security policy with user-defined rules

In this codelab, you will learn about Google Cloud Armor named IP address lists. More specifically, you will configure a named IP address list within a security policy and validate connectivity.

In this codelab, you will learn about Cloud Armor’s preconfigured WAF rules which allow you to protect against some of the top 10 OWASP vulnerabilities with simple names for rule sets.

In this codelab, you’ll compare common Cassandra queries for inserting, updating, reading and deleting data to their Cloud Bigtable equivalent with the Java client.

In this codelab, you’ll learn how to populate a Google spreadsheet from a Cloud Function reacting to a CSV file upload to Cloud Storage

In this codelab, you will learn about Cloud IDS. More specifically, you will create a Cloud IDS endpoint, generate threat traffic, and analyze the results.

In this codelab, you’ll build a basic Java application that uses Hibernate to persist data in Cloud Spanner.

In this codelab, you’ll learn how to use Terraform to create and manage Google Cloud Spanner resources.

In this codelab, you learn how to create a gaming leaderboard using a Cloud Spanner database table with a commit timestamp column.

In this codelab, you learn how to create a gaming leaderboard using a Cloud Spanner database table with a commit timestamp column.

In this codelab, you learn how to create a gaming leaderboard using a Cloud Spanner database table with a commit timestamp column.

In this codelab, you’ll create a Google Cloud Spanner instance, an empty database, and learn how to load sample data and query it.

Learn to connect your Spring Boot app to Cloud SQL.

In this codelab you’ll learn how to connect an AlloyDB cluster to an Oracle database deployed in a separate network connected through VPN.

In this codelab, you’ll setup a connection between a Compute Engine instance and Cloud SQL within Google’s private internal network using the Cloud SQL Proxy.

In this codelab, you’ll setup a simple connection to a Cloud SQL instance accessible from anywhere.

Learn to build and publish an optimized Docker image for a Spring Boot Kotlin app in no time without Docker or Dockerfile, then run the built image on Cloud Run.

Learn to continuously deploy containerized workloads to GKE with Cloud Build.

In this codelab, you will use Vertex AI Conversation to create, configure, and deploy a Data Store Agent and chat app to answer customer questions about products in the Google Store.

In this codelab, you will use the Google Docs API to create a Google Doc, and write the transcript of an audio file to this document. You’ll use the Speech-to-Text API to obtain the text transcript of the given audio file.

In this codelab, you’ll learn how to create a custom visualization that you can use in a Looker Studio report.

In this codelab, you’ll use dscc-gen, a project templating tool, to create a Community Visualization for Data Studio.

In this lab you will create a CDN distribution and apply Cloud Armor rules to protect your edge cache.

In this codelab, you will learn how to use the Google Cloud Translation API with Python and either run locally or deploy to a Cloud serverless compute platform (App Engine, Cloud Functions, or Cloud Run).

In this codelab, you will learn how to use the Google Cloud Translation API with Python and either run locally or deploy to a Cloud serverless compute platform (App Engine, Cloud Functions, or Cloud Run).

In this codelab, you will learn how to use the Google Cloud Translation API with Python and either run locally or deploy to a Cloud serverless compute platform (App Engine, Cloud Functions, or Cloud Run).

In this codelab, you will learn how to use the Google Cloud Translation API with Python and either run locally or deploy to a Cloud serverless compute platform (App Engine, Cloud Functions, or Cloud Run).

In this codelab, you will learn how to use the Google Cloud Translation API with Node.js and either run locally or deploy to a Cloud serverless compute platform (App Engine, Cloud Functions, or Cloud Run).

Learn how to deploy a Lustre Parallel file system in Google Cloud Platform using the open source Lustre Deployment Manager scripts.

In this codelab, you learn how to turn your Micronaut microservices into a replicated service running on Google Kubernetes Engine.

Learn to build a simple Spring Boot app and deploy it to App Engine standard environment.

Learn to deploy a Spring Boot Java app to Kubernetes on GKE.

Learn to deploy a website with Cloud Run, which brings serverless agility to your containerized apps.

In this code lab, you’ll learn how to deploy a simple ASP.NET Core app to Google App Engine

Learn how to provision a dynamically scalable HPC cluster using Google Compute Engine, Google Deployment Manager, and the Slurm Workload Manager.

Learn to deploy and run a container with Cloud Run on Node.js.

Microsoft.NET Core is an open-source and cross-platform version of.NET that can natively run in containers..NET Core is available on GitHub and is maintained by Microsoft and the.NET community. This lab deploys a containerized.NET Core app into

In this code lab, you learn how to deploy a simple ASP.NET app to a Windows Server on Google Compute Engine

In this codelab, you learn how to deploy your ASP.NET Core app to Google Kubernetes Engine with Istio.

In this codelab, you continue working on an ASP.NET Core application from part 1 and add more Istio features to it.

In this codelab, you learn how to turn your ASP.NET Core code into a replicated application running on Kubernetes on Google Kubernetes Engine.

In this code lab, you’ll learn how to create a Google Compute Engine virtual machine with Windows Server and the ASP.NET Framework.

Learn to deploy, scale, and update your website with GKE.

Learn how to build a retail chatbot with Dialogflow CX, a conversational AI platform (CAIP) for building virtual agents

In this codelab, you will build simple Spring Boot microservices and add distributed tracing capabilities using Spring Cloud GCP Trace starter that stores trace data on Cloud Trace.

In this codelab, you will learn how to use Document AI Workbench to create fully customized models using your own training data.

In this codelab, you will learn how to use Document AI Uptraining to improve model quality using your own training data.

In this codelab, you will learn how to use Document AI Human in the Loop to complete Human Review tasks with Specialized Processors.

In this codelab you will set up a Google Cloud project to use in the following codelabs. You will also learn how to use Cloud Shell for editing files and running terminal commands.

In this codelab, you will encrypt and decrypt data using Cloud KMS

In this codelab, you will encrypt and decrypt data using Cloud KMS asymmetric keys.

use-cmek-to-encrypt-cloud-functions

Learn to enhance your Action with features that keep users coming back to it.

In this codelab, you will learn about Events for Cloud Run. More specifically, you will listen to events from Cloud Pub/Sub, Audit Logs, Cloud Storage, Cloud Scheduler and how to produce/consume custom events.

In this lab you’ll build a tf.keras for identifying fraudulent transactions using TensorFlow, and then interpret the model’s results with Cloud’s Explainable AI SDK.

In this codelab, you will learn how to use Private Service Connect to chain L7 Load Balancer.

Learn how to use App Engine bundled services in Gen2 runtimes

In this codelab, you will learn about the advanced traffic features which are available in the new External HTTPs Load Balancer.

In this codelab, you will learn about the advanced traffic features which are available in the new External HTTPs Load Balancer.

In this codelab, you will learn how to use the Document AI Form Parser to parse a handwritten form with Python. We will use a simple medical intake form as an example, but this procedure will work with any generalized form supported by DocAI. This

In this lab, you will learn how to build an end-to-end data to AI system for real-time fraud detection on Google Cloud. The goal is to understand how to go from raw data to having a production-ready ML pipeline running on Google Cloud. This lab uses

In this codelab, you will learn how to build and deploy complex data science pipelines with hyperparameter tuning on Kubeflow Pipelines, without using any CLI commands or SDKs.

In this codelab, you will learn how to build and deploy complex data science pipelines with Kubeflow Pipelines, without using any CLI commands or SDKs.

In this codelab, you’ll chat with your users, ask questions about your documentation, or extend a model with function calling, using Generative AI in Java, integrating the Gemini large language model on Vertex AI, and leveraging the LangChain4j framework

In this codelab, you’ll chat with your users, or ask questions about your documentation, using Generative AI in Java, integrating the PaLM large language model, and leveraging the LangChain4J LLM orchestration framework

In this codelab, you’ll get started with Generative AI in Java, integrating the PaLM large language model, and leveraging the LangChain4J LLM orchestration framework

In this lab, you'll use Vertex AI get predictions from a pre-trained image classification model. You'll learn how to: The total cost to run this lab on Google Cloud is about $1. This lab uses the newest AI product offering available on Google Cloud.

In this tutorial, you will learn how to build a simple web app with Python on Google App Engine.

In this codelab, you’ll learn how to use BigQuery to create machine learning models for Analytics 360 datasets.

Get started with Cloud Functions, Google’s serverless execution environment for building and connecting cloud services. This hands-on lab shows you how to create, deploy, and test a cloud function using the Google Cloud console

In this codelab, you will learn about Google Cloud Functions (2nd gen). More specifically, you will deploy functions that respond to HTTP calls, Pub/Sub messages, Cloud Storage events and Cloud Audit Logs.

In this codelab, you will first explore a Node.js application to take screenshots of web pages and store them to Cloud Storage. You will then build a container image for the application, run it as a job on Cloud Run, update the job to process more web pages and run the job on a schedule with Cloud Scheduler.

In this codelab, you will learn how to connect to computing resources hosted on Google Cloud Platform via the web.

In this codelab, you learn how to deploy Managed Active Directory on Google Cloud Platform

In this codelab you’ll learn how to use Cloud SQL AI integration in combination with vector search and create an index on the vector data

In this codelab you’ll learn how to use AlloyDB AI in combination with vector search and create an index on the vector data

In this codelab, you will learn about Google Cloud Run functions in C#. More specifically, you will deploy C# functions responding to HTTP and CloudEvents from various Google Cloud sources.

In this codelab, you’ll become familiar with Google Compute Engine by spinning up VMs, installing software, and making those available on the Internet via a network load balancer.

This codelab shows you how to use the Google Cloud Console to grant Identity and Access Management (IAM) roles to principals for your project.

Learn how to build a custom Dialogflow chatbot for Google Chat with dynamic card responses.

In this codelab, you’ll learn how to get started with Cloud Run by deploying and running a stateless container serverless-ly (with the infrastructure abstracted away). Cloud Run offers a fully-managed option as well as the ability to run on top of a GKE cluster.

In this tutorial, you’ll learn how to get started with Cloud Run by deploying and running a stateless container serverless-ly (with the infrastructure abstracted away). Cloud Run offers a fully-managed option as well as the ability to run on top of a GKE cluster.

Learn to host and scale a web app in Google Cloud with Compute Engine.

This lab builds on the concept of fulfillment in Dialogflow. You learn to create dataset and table in BigQuery then set up BigQuery integration details in Dialogflow fulfillment and test the conversational experience.

In this codelab, you will use function calling in Gemini to build an app for users to ask about exchange rates, fetch the latest data from an external API, then respond to the user with the answer.

In this codelab, you’ll learn how to use Confidential Space to transact digital assets with multi-party computation.

Learn how to add blobstore usage to a simple Python 2 App Engine app

Learn how to add Memcache usage to a basic Python 2 Flask App Engine NDB app.

Learn how to add Task Queue pull task usage to a basic Python 2 Flask App Engine NDB app.

Learn how to add Task Queue push task usage to a basic Python 2 Flask App Engine NDB app.

In this tutorial, you’ll build HTTP Cloud Functions in Python.

In this codelab, developers build a cloud-based image processing workflow in Python using both Google Workspace (formerly G Suite) & Google Cloud APIs. Specifically, you’ll be downloading an image file from Google Drive, archiving it to Google Cloud Storage, analyzing its contents with Google Cloud Vision, and generating report data in Google Sheets.

In this beginner-friendly codelab, you’ll become familiar with the generative fallback feature that uses Google’s latest generative large language models (LLMs) to generate virtual agent responses.

In this beginner-friendly codelab, you’ll become familiar with the generator feature. Generators use Google’s latest generative large language models (LLMs) to generate agent responses and in the context of this codelab you’ll use a generator that pulls information from a data store to make informed decisions.

In this codelab, we will implement a data ingestion pattern to load CSV formatted healthcare data into BigQuery using Cloud Data Fusion in real time.

In this codelab, we will implement a data ingestion pattern to load CSV formatted healthcare data into BigQuery using Cloud Data Fusion.

In this codelab, we will implement a data ingestion pattern to load FHIR - R4 formatted healthcare data (Regular Resources) into BigQuery using Cloud Healthcare FHIR APIs.

In this code lab, you learn how to install and use Cloud tools for Windows PowerShell.

In this code lab, you learn how to install and use Cloud tools for Visual Studio.

In this codelab, you’ll build and deploy a Toolbox for your Price Prediction Application that uses AlloyDB and generative AI features using Gen AI Toolbox for Databases service.

OpenTelemetry is the industry standard for system observability on trace and metrics. This session shares the knowledge on how to instrument application metrics using OpenTelemetry and how to utilize them on Cloud Monitoring and other monitoring tools.

Learn to integrate the Vision API with Dialogflow.

In this codelab you will augment an Appointment Scheduler Chat app backed by Dialogflow and Google Calendar to run on Google Chat. You will build and deploy custom Google Chat messages.

In this lab, you will learn how to create and run ML pipelines with Vertex Pipelines. You'll learn how to: The total cost to run this lab on Google Cloud is about $25. This lab uses the newest AI product offering available on Google Cloud. Vertex AI

In this codelab, you’ll get introduced to Cloud Bigtable with the Java HBase client. You’ll load in data, then run some queries and plot the data on a map..

In this codelab, you’ll learn about Google Cloud Operations Suite. The lab will involve installing a sample application using gcloud. Once the sample application is deployed, you will then use Cloud Monitoring to define Dashboards, Alerts, Uptime Checks and more.

Query Insights for Cloud SQL helps you detect, diagnose, and prevent query performance problems for Cloud SQL databases. It provides self-service, intuitive monitoring, and diagnostic information that goes beyond detection to help you to identify the

In this codelab, you will learn how to orchestrate and automate Google Cloud and HTTP-based API services with Workflows.

Use Gemini Code Assist to help you write tests for your code

In this codelab, you will set up a Cloud AI Platforms Pipeline installation (Hosted KFP) with GKE, build and run ML workflows using Kubeflow Pipelines, and define and run pipelines from within an AI Platform Notebook (Jupyter).

Learn how to build and deploy a LangChain app on Cloud Run

Learn how to secure Cloud Functions by requiring authentication for invocation

Learn how to secure Cloud Run functions by requiring authentication for invocation

Learn to load and query data with bq, a Python-based command-line tool for BigQuery.

Learn how to code, deploy, and debug Cloud Functions for Node.js from within Visual Studio Code on your local machine.

In this lab, you'll use Vertex AI to build a pipeline that trains a custom Keras Model in TensorFlow. We will then use the new functionality available in Vertex AI Experiments to track and compare model runs in order to identify which combination of

In this tutorial, you will learn to manage Document AI processors with Python.

In this codelab, you’ll build two Spring Boot applications that exchange messages through Spring Integration, using Google Cloud Pub/Sub on the background.

Get to know Google Cloud by deploying a microservice on Cloud Run and joining a virtual rumpus where your microservice will throw “rainbows” at other microservices, competing to win! You will get hands on deploying Kotlin, Java, Go, Python, or Node.js microservices, learning about containers and Cloud Run along the way. See if you can score more points than other Adventurers by continually improving your algorithm.

In this codelab, you will learn how to migrate a Python 2 App Engine Cloud NDB and Cloud Tasks (v1) app to Python 3, Cloud Datastore, and Cloud Tasks (v2)

Learn how to migrate blobstore usage to Cloud Storage for a Python 2 App Engine ndb app.

Learn how to migrate a Python 2 App Engine NDB & Memcache app to Cloud NDB & Cloud Memorystore (for Redis), followed by an upgrade to Python 3

Learn how to migrate a Python 2 App Engine NDB & Task Queue (pull tasks) app to Cloud NDB & Cloud Pub/Sub, followed by an upgrade to Python 3

Learn how to migrate a Python 2 App Engine NDB & Task Queue (push tasks) app to Cloud NDB & Cloud Tasks

Learn how to migrate a Python 2 App Engine NDB & Users service app to Cloud NDB & Cloud Identity Platform, followed by an upgrade to Python 3

This lab leads you through breaking down a monolithic website into microservices and deploying them to Google Kubernetes Engine.

In this codelab, you’ll migrate a simple web server from Compute Engine to Kubernetes Engine using Migrate for Anthos.

Learn how to convert a simple Java App Engine app, containerize it with Buildpacks, and move it to Cloud Run

Learn how to convert a simple Java App Engine app, containerize it with Docker, and move it to Cloud Run

Learn how to convert a simple Java App Engine app, containerize it with Jib, and move it to Cloud Run

Learn how to convert a simple Python App Engine app (or break-up a larger, monolithic app into microservices) and move it to Cloud Functions

Learn how to migrate Python App Engine app from webapp2 to the Flask web framework.

Learn how to migrate a simple App Engine app from ndb to Cloud NDB.

Learn how to migrate a simple App Engine app from to Cloud NDB to Cloud Datastore.

Learn how to containerize and migrate a simple App Engine app to Cloud Run using Docker

Learn how to containerize and migrate a simple App Engine app to Cloud Run using Cloud Buildpacks.

Learn how to migrate a simple App Engine app from to Cloud Datastore to Cloud Firestore.

In this codelab, you will perform Optical Character Recognition (OCR) of PDF documents using Document AI and Python. You will explore how to make both Online (Synchronous) and Batch (Asynchronous) process requests.

In this codelab, you’ll learn how to leverage Dataprep, BigQuery and Looker to analyze the impact of different retail prices and make informed decisions to optimize the price of products.

In this codelab, you will use the BigQuery web UI to understand partitioning and clustering in BigQuery

In this codelab, you will learn to configure a network load balancer to distribute traffic across the load balancer’s backend instances based on the weights reported by an HTTP health check using weighted load balancing.

In this code lab, you create a Cloud Function that runs image analysis using Vision API in response to Cloud Storage events and saves the analysis results in a Firestore collection.

In this code lab, you create a Cloud Function (Java) that runs image analysis using Vision API in response to Cloud Storage events and saves the analysis results in a Firestore collection.

In this code lab, you create a Cloud Run service that creates thumbnails of pictures in response to Cloud Storage events passed through a Pub/Sub topic.

In this code lab, you create a Cloud Run service triggered by Cloud Scheduler on a regular time interval to create a collage of the most recent pictures.

In this codelab, you create a web frontend on Google App Engine that lets users upload pictures, browse the uploaded pictures, their thumbnails and the latest collage.

In this code lab, you create a new Cloud Run service that is triggered by Eventarc when an image is deleted in Cloud Storage. In turn, the service deletes the image thumbnail in Cloud Storage and metadata in Firestore collection.

In this code lab, you create an orchestrated version of Pic-a-daily using Workflows

This lab shows you how to use PySpark on Dataproc to load data from BigQuery and save it to Google Cloud Storage.

In this codelab, you will learn about Private Service Connect for Google APIs. More specifically, you will create a service endpoint for storage APIs, create a cloud storage bucket & perform validation using DNS.

In this codelab, you will learn about Private Service Connect for Google APIs. More specifically, you will create a service endpoint for storage APIs, create a cloud storage bucket & perform validation using DNS.

In this lab, you'll use Vertex AI to run a distributed training job on Vertex AI Training using TensorFlow. This lab is part of the Prototype to Production video series. Be sure to complete the previous labs before trying out this one. You can watch

In this lab, you'll use Vertex AI to get online and batch predictions from a custom trained model. This lab is part of the Prototype to Production video series. Be sure to complete the previous lab before trying out this one. You can watch the

In this lab, you'll use Vertex AI to run a hyperparameter tuning job on Vertex AI Training. This lab is part of the Prototype to Production video series. Be sure to complete the previous lab before trying out this one. You can watch the accompanying

In this lab, you'll use Vertex AI to run a custom training job. This lab is part of the Prototype to Production video series. You'll build an image classification model using the Flowers dataset. You can watch the accompanying video to learn more:.

In this lab you’ll learn how you can use AI Platform Notebooks for prototyping your machine learning workflows. We’ll cover creating a custom notebooks instance, tracking your notebook code in git, and debugging models with the What-If Tool.

In this codelab, you will learn how to start a managed Spark/Hadoop cluster using Dataproc, submit a sample Spark job, and shut down your cluster using the command line.

This lab shows you how to use Spark MLlib and spark-nlp for performing machine learning and NLP on large quantities of data.

In this code lab, you’ll learn the basics of BigQuery and how to use it to query terabytes of data, like the Wikipedia dataset.

In this codelab, you will create a load balancer and associated backend service. You will then create a Cloud Armor rate limiting policy and understand how it protects your backends.

In this codelab, you will build simple Spring Boot microservices and retrieve secrets / configuration values stored in Secret Manager.

In this codelab, you will learn how to route Datadog monitoring alerts to Google Cloud with Eventarc.

In this codelab, you will learn how to respond to Datadog monitoring alerts with Eventarc and Workflows

You will use Cloud Dataflow, create a Maven project with the Cloud Dataflow SDK, and run a distributed work count pipeline using the Google Cloud Platform Console.

In this codelab, you will see how to run BigQuery jobs against a Wikipedia dataset in parallel with the parallel iteration feature of Workflows.

In this lab, you will learn how to run a custom model training job using the Kubeflow Pipelines SDK on Vertex Pipelines. You'll learn how to: The total cost to run this lab on Google Cloud is about $5. This lab uses the Vertex AI, our end-to-end

The page explains how to use Dataflow SQL and create Dataflow SQL jobs.

Learn how to search for and select Google APIs, and view their metrics.

In this codelab, you’ll learn how to secure multi-party data sharing while preserving confidentiality using Confidential Space

In this codelab, you’ll learn how to secure machine learning models and intellectual property using Confidential Space

Demonstrates how to use Binary Authorization secure your GKE cluster by requiring all containers to be verified and signed by trusted attestors as a part of the build/deploy process

In this code lab, you develop a Web API on top of Google Cloud serverless solutions, to serve a bookshelf and its books. You will create a Cloud Function to import sample data, a Cloud Run container to offer a reusable backend Web API, and an App Engine web application to offer a web frontend to browse through the library of books.

Learn how to create a Google Cloud project, set up and navigate the Google Cloud Console, and access some of the most commonly used services in Cloud Console.

In this codelab, you will encrypt and decrypt data using Cloud KMS asymmetric keys.

In this codelab, you’ll learn how to leverage the signed container image feature to improve usability of Confidential Space.

In this codelab, you will learn how to use Procurement DocAI and Lending DocAI to classify and parse specialized documents.

In this codelab, you will learn how to use Spring Cloud GCP to write and read objects from Datastore

In this codelab, you will learn how to use Spring Cloud GCP to write and read data from a Cloud Spanner database.

Spring Native is an emerging project set to enter the mainline in Spring 6.x and Spring Boot 3.x, which means that now is the perfect time to get familiar with it a few months ahead of its release.

In this codelab, you will create a TCP/SSL load balancer with a backend service and limit access to the load balancer to only a specific set of user clients.

In this codelab, you will teach the computer to recognise handwritten digits with 99% accuracy, in 100 lines of Python / Keras code.

In this lab, you’ll learn about how to solve time-series problems using Vertex AI, covering Notebooks, Training, Prediction, and BigQuery ML.

In this codelab, you’ll build a RAG based Vector Search application designed to find matching toys for customer search (through texts and images), create custom toys based on user request and predict price for the custom-created toy using AlloyDB, Gemini, Imagen, LangChain4j and GenAI Toolbox for Databases.

In this lab you’ll learn how to train your model in the Cloud with hyperparameter tuning. We’ll be showing you how to do this with PyTorch, but you can do this in any framework you’d like.

In this codelab, you’ll learn how to transform your Google Forms survey data using Dataprep and push it into BigQuery for deeper analysis

This guide provides instructions for transitioning an existing network load balancer from a target pool backend to a regional backend service.

In this codelab, you will learn about the main features of Eventarc.

In this codelab, you will listen to events from Pub/Sub, Cloud Storage and Cloud Audit Logs with Eventarc and pass them to a Kubernetes service running on Google Kubernetes Engine (GKE).

This CodeLab demonstrates use of Gemini for Google Cloud to accelerate troubleshooting and issue remediation. You will get familiar with Gemini in summarizing logs, explaining errors and assisting in finding solutions for the problem.

In this codelab, you’ll learn how to run AI/ML workload with an accelerator using the Trusted Space solution.

Learn about entities in Dialogflow.

Learn the concept of fulfillment in Dialogflow.

Learn the basics of BigQuery and how to query terabytes of public data by using GitHub commit data as an example.

In this codelab, you’ll learn how to secure multi-party data sharing while preserving confidentiality using Confidential Space. This codelab focuses on how to use Confidential Space with protected resources that are hosted somewhere other than Google Cloud. You will learn how to request a custom token from the Google Attestation Service by providing a nonce, audience and a PKI token type.

Learn to use Stackdriver Logging and Stackdriver Trace for Cloud Functions.

In this codelab you will create a web application, restrict access to only specified groups of users, use the authenticated user’s identity in the program

In this codelab, you will learn how to use BigQuery with C#

In this codelab, you will learn how to use BigQuery with the Node.js client library

In this codelab, you will learn how to use BigQuery with Python

The goal of this lab is to explore Cloud NAT’s Dynamic Port Allocation (DPA) Feature.

The goal of this lab is to explore Cloud NAT’s Rules feature

In this codelab, you will use Document AI Warehouse to ingest, process, and search the full text of documents.

In this codelab, you will learn how to use an External HTTP(s) Hybrid Load Balancer to reach a Network Endpoint Group (NEG).

In this codelab, we shall look at an existing Jump Start Solution, AI Summarization, that uses Vertex AI models to summarize PDF documents that have been uploaded to Google Cloud Storage. We will use Gemini Code Assist to understand and add new functionality to the solution.

Setting up and running a notebook with interactive Beam

In this codelab, you will learn how to use Private Service Connect to publish and consume services.

In this codelab, you will learn how to use Private Service Connect to Publish and Consume services in a GKE environment

In this tutorial, you will learn to use Secret Manager with Python

In this codelab, you will learn to use the Natural Language API with C#

In this tutorial, you will learn to use the Natural Language API with Python.

In this codelab, you will learn to use the Speech-to-Text API with C#

In this codelab, you will transcribe an audio file to using the Speech-to-Text API with Node.js

In this tutorial, you will learn to use the Speech-to-Text API with Python.

In this codelab, you will learn to use the Text-to-Speech API with C#

In this codelab, you will transcribe an audio file to using the Text-to-Speech API with Node.js

In this tutorial, you will learn to use the Text-to-Speech API with Python.

In this codelab, you will learn to use the Translation API with C#

In this tutorial, you’ll learn to use the Translation API with Python.

In this codelab, you will learn to use the Video Intelligence API with C#

In this tutorial, you will learn to use the Video Intelligence API with Python.

In this codelab, you will learn to use the Google Cloud Vision API with C#.

In this tutorial, you will learn to use the Vision API with Python.

In this lab, you will learn how to analyze metadata from your Vertex Pipelines runs with Vertex ML Metadata. You'll learn how to: The total cost to run this lab on Google Cloud is about $2. This lab uses the newest AI product offering available on

In this lab, you'll learn how to configure and launch notebook executions with Vertex AI Workbench. You'll learn how to: The total cost to run this lab on Google Cloud is about $2. This lab uses the newest AI product offering available on Google

In this lab, you'll learn how to use Vertex AI Workbench for data exploration and ML model training. You'll learn how to: The total cost to run this lab on Google Cloud is about $1. This lab uses the newest AI product offering available on Google

In this lab, you will use Vertex AI to train and serve a model with tabular data. This is the newest AI product offering on Google Cloud, and is currently in preview. You'll learn how to: The total cost to run this lab on Google Cloud is about $22.

In this lab, you'll use the co-hosting model feature in Vertex AI to host multiple models on the same VM for online predictions. You'll learn how to: The total cost to run this lab on Google Cloud is about $2. This lab uses the newest AI product

The focus of this demo is you can use Vertex AI to train and deploy a ML model. It assumes that you are familiar with Machine Learning even though the machine learning code for training is provided to you. You will use Datasets for dataset creation

In this lab, you'll learn how to use Vertex AI for hyperparameter tuning and distributed training. While this lab uses TensorFlow for the model code, the concepts are applicable to other ML frameworks as well. You'll learn how to: The total cost to

In this lab, you will train a model with BigQuery Machine Learning, and then export and deploy that model to Vertex AI. This is the newest AI product offering on Google Cloud. You'll learn how to: The total cost to run this lab on Google Cloud is

In this lab, you'll use Vertex AI to run a hyperparameter tuning job for a TensorFlow model. While this lab uses TensorFlow for the model code, the concepts are applicable to other ML frameworks as well. You'll learn how to: The total cost to run

In this lab, you'll use Vertex AI to run a multi-worker training job for a TensorFlow model. You'll learn how to: The total cost to run this lab on Google Cloud is about $5. This lab uses the newest AI product offering available on Google Cloud.

In this lab, you will use Vertex AI to train and serve a TensorFlow model using code in a custom container. While we're using TensorFlow for the model code here, you could easily replace it with another framework. You'll learn how to: The total cost

In this lab, you'll learn how to run a custom training job on Vertex AI Training with the autopackaging feature. Custom training jobs on Vertex AI use containers. If you do not want to build your own image, you can use auotpackaging, which will build

In this lab, you'll learn how to use custom prediction routines on Vertex AI to write custom preprocessing and postprocessing logic. While this sample uses Scikit-learn, custom prediction routines can work with other Python ML frameworks such as

In this codelab, we demonstrate a solution to access and visualize clinical data stored in BigQuery using Looker.

In this codelab, you will learn how to connect your BigQuery data to Data Studio, and develop different visualization types based on the structure of your data. This includes visualizations that leverage Data Studio’s new support for Google maps.

In this lab, we will create a VPC Service Controls perimeter and use it to protect a project. We will then provoke a VPC Service Controls ingress violation and walk through the process of troubleshooting the denial. By the end of this lab, you will have a better understanding of how to use VPC Service Controls to protect your resources.

In this lab, we will create a VPC Service Controls perimeter and use it to protect a project. We will then provoke a VPC Service Controls egress violation and walk through the process of troubleshooting the denial by creating an egress rule. By the end of this lab, you will have a better understanding of how to use VPC Service Controls to protect your resources.

In this codelab, we shall look at utilizing the Help Me Write feature to author Synthetic Monitoring Tests for your existing services