Dados tabulares do Hello: configurar o projeto e o ambiente
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Neste tutorial, apresentamos as etapas necessárias para treinar e receber previsões do seu modelo de dados tabulares no console Google Cloud .
Se você planeja usar o SDK da Vertex AI para Python, verifique se a conta de serviço
que inicializa o cliente tem o papel do IAM do
Agente de serviço da Vertex AI
(roles/aiplatform.serviceAgent).
Nesta parte do tutorial, você vai configurar seu projeto Google Cloud para usar
a Vertex AI e um bucket do Cloud Storage que contém os documentos
para treinar seu modelo do AutoML.
Configurar o projeto e o ambiente
In the Google Cloud console, go to the project selector page.
Abra o Cloud Shell.
O Cloud Shell é um ambiente shell interativo para Google Cloud que permite gerenciar projetos e recursos a partir do navegador da Web.
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.
No campo Novos principais, digite seu identificador de usuário.
Normalmente, é o endereço de e-mail de uma Conta do Google.
Na lista Selecionar papel, escolha um.
Para conceder outros papéis, clique em addAdicionar
outro papel e adicione cada papel adicional.
Clique em Salvar.
O papel do IAM Usuário da Vertex AI (roles/aiplatform.user)
fornece acesso para usar todos os recursos na Vertex AI. O papel Administrador do Storage (roles/storage.admin) permite armazenar o conjunto de dados de treinamento do documento no Cloud Storage.
[[["Fácil de entender","easyToUnderstand","thumb-up"],["Meu problema foi resolvido","solvedMyProblem","thumb-up"],["Outro","otherUp","thumb-up"]],[["Difícil de entender","hardToUnderstand","thumb-down"],["Informações incorretas ou exemplo de código","incorrectInformationOrSampleCode","thumb-down"],["Não contém as informações/amostras de que eu preciso","missingTheInformationSamplesINeed","thumb-down"],["Problema na tradução","translationIssue","thumb-down"],["Outro","otherDown","thumb-down"]],["Última atualização 2025-08-19 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."]]
