Datos tabulares de Hello: Configura tu proyecto y tu entorno
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En este instructivo, se explican los pasos necesarios para entrenar y obtener predicciones
de tu modelo de datos tabulares en la Google Cloud consola.
Si planeas usar el SDK de Vertex AI para Python, asegúrate de que la cuenta de servicio que inicializa el cliente tenga el rol
Vertex AI Service Agent
(roles/aiplatform.serviceAgent) de IAM.
En esta parte del instructivo, configurarás tu proyecto Google Cloud para usar Vertex AI y un bucket de Cloud Storage que contiene los documentos para entrenar tu modelo de AutoML.
Configure su proyecto y su entorno.
In the Google Cloud console, go to the project selector page.
Abre Cloud Shell.
Cloud Shell es un entorno de shell interactivo
para Google Cloud que te permite administrar proyectos y recursos desde
el navegador web.
En Cloud Shell, establece el proyecto actual como tu ID del proyecto Google Cloud
y guárdalo en la variable de shell projectid:
gcloud config set project PROJECT_ID &&
projectid=PROJECT_ID &&
echo $projectid
Reemplaza PROJECT_ID por el ID del proyecto. Puedes
ubicar el ID del proyecto en la consola de Google Cloud . Para obtener más información, consulta
Encuentra el ID del proyecto.
Enable the IAM, Compute Engine, Notebooks, Cloud Storage, and Vertex AI APIs.
In the Principal column, find all rows that identify you or a group that
you're included in. To learn which groups you're included in, contact your
administrator.
For all rows that specify or include you, check the Role column to see whether
the list of roles includes the required roles.
En el campo Principales nuevas, ingresa tu identificador de usuario.
Esta suele ser la dirección de correo electrónico de una Cuenta de Google.
En la lista Seleccionar un rol, elige un rol.
Para otorgar funciones adicionales, haz clic en addAgregar otro rol y agrega cada rol adicional.
Haz clic en Guardar.
El rol de IAM de usuario de Vertex AI (roles/aiplatform.user)
proporciona acceso para usar todos los recursos en Vertex AI. La función Administrador de almacenamiento
(roles/storage.admin) te permite almacenar el conjunto de datos
de entrenamiento del documento en Cloud Storage.
[[["Fácil de comprender","easyToUnderstand","thumb-up"],["Resolvió mi problema","solvedMyProblem","thumb-up"],["Otro","otherUp","thumb-up"]],[["Difícil de entender","hardToUnderstand","thumb-down"],["Información o código de muestra incorrectos","incorrectInformationOrSampleCode","thumb-down"],["Faltan la información o los ejemplos que necesito","missingTheInformationSamplesINeed","thumb-down"],["Problema de traducción","translationIssue","thumb-down"],["Otro","otherDown","thumb-down"]],["Última actualización: 2025-09-04 (UTC)"],[],[],null,["# Hello tabular data: Set up your project and environment\n\nThis tutorial walks you through the required steps to train and get predictions\nfrom your tabular data model in the Google Cloud console.\nIf you plan to use the Vertex AI SDK for Python, make sure that the service account\ninitializing the client has the\n[Vertex AI Service Agent](/vertex-ai/docs/general/access-control#aiplatform.serviceAgent)\n(`roles/aiplatform.serviceAgent`) IAM role.\n\nFor this part of the tutorial, you set up your Google Cloud project to use\nVertex AI and a Cloud Storage bucket that contains the documents\nfor training your AutoML model.\n\nSet up your project and environment\n-----------------------------------\n\n1. In the Google Cloud console, go to the project selector page.\n\n [Go to project selector](https://console.cloud.google.com/projectselector2/home/dashboard)\n2. Select or create a Google Cloud project.\n\n | **Note**: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.\n3.\n [Verify that billing is enabled for your Google Cloud project](/billing/docs/how-to/verify-billing-enabled#confirm_billing_is_enabled_on_a_project).\n\n4. Open [Cloud Shell](/shell/docs/launching-cloud-shell-editor). Cloud Shell is an interactive shell environment for Google Cloud that lets you manage your projects and resources from your web browser.\n[Go to Cloud Shell](https://ssh.cloud.google.com/cloudshell/editor)\n5. In the Cloud Shell, set the current project to your Google Cloud project ID and store it in the `projectid` shell variable: \n\n ```\n gcloud config set project PROJECT_ID &&\n projectid=PROJECT_ID &&\n echo $projectid\n ```\n Replace \u003cvar translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e with your project ID. You can locate your project ID in the Google Cloud console. For more information, see [Find your project ID](/vertex-ai/docs/tutorials/tabular-bq-prediction/prerequisites#find-project-id).\n6.\n\n\n Enable the IAM, Compute Engine, Notebooks, Cloud Storage, and Vertex AI APIs.\n\n\n [Enable the APIs](https://console.cloud.google.com/flows/enableapi?apiid=iam.googleapis.com, compute.googleapis.com,notebooks.googleapis.com storage.googleapis.com aiplatform.googleapis.com)\n7. \n8.\n\n Make sure that you have the following role or roles on the project:\n\n roles/aiplatform.user, roles/storage.admin\n\n #### Check for the roles\n\n 1.\n In the Google Cloud console, go to the **IAM** page.\n\n [Go to IAM](https://console.cloud.google.com/projectselector/iam-admin/iam?supportedpurview=project)\n 2. Select the project.\n 3.\n In the **Principal** column, find all rows that identify you or a group that\n you're included in. To learn which groups you're included in, contact your\n administrator.\n\n 4. For all rows that specify or include you, check the **Role** column to see whether the list of roles includes the required roles.\n\n #### Grant the roles\n\n 1.\n In the Google Cloud console, go to the **IAM** page.\n\n [Go to IAM](https://console.cloud.google.com/projectselector/iam-admin/iam?supportedpurview=project)\n 2. Select the project.\n 3. Click person_add **Grant access**.\n 4.\n In the **New principals** field, enter your user identifier.\n\n This is typically the email address for a Google Account.\n\n 5. In the **Select a role** list, select a role.\n 6. To grant additional roles, click add **Add\n another role** and add each additional role.\n 7. Click **Save**.\n9. The Vertex AI User (`roles/aiplatform.user`) IAM role provides access to use all resources in Vertex AI. The [Storage Admin](/storage/docs/access-control/iam-roles) (`roles/storage.admin`) role lets you store the document's training dataset in Cloud Storage.\n\n\u003cbr /\u003e\n\nWhat's next\n-----------\n\nFollow the [next page of this tutorial](/vertex-ai/docs/tutorials/tabular-automl/dataset-train) to\ncreate a tabular dataset and train a classification model."]]
