Carica il set di dati di test in BigQuery o
Cloud Storage. Il set di dati di test deve contenere i dati empirici reali, ovvero il risultato effettivo previsto per un'inferenza. Ottieni il link
al file o l'ID del set di dati.
Nella scheda Valuta, fai clic su Crea valutazione.
Inserisci un nome della valutazione.
Seleziona un obiettivo, ad esempio classificazione o regressione.
Inserisci il nome della colonna target di valutazione, ovvero la colonna dei dati di addestramento che il modello è addestrato a prevedere.
Per Seleziona origine, seleziona l'origine del set di dati di test.
In Tabella BigQuery, inserisci il percorso BigQuery.
In File su Cloud Storage, inserisci il percorso Cloud Storage.
Per Output di previsione batch, seleziona un formato di output.
Inserisci il percorso BigQuery o l'URI Cloud Storage.
Fai clic su Inizia valutazione.
Python
Per visualizzare il flusso di lavoro di valutazione del modello dell'API Vertex AI in
Vertex AI Pipelines, consulta i notebook di esempio per i seguenti tipi di
modello:
L'SDK per la valutazione dei modelli con Vertex AI è in
versione sperimentale. Per registrarti alla versione sperimentale, compila il
modulo di onboarding.
Vertex AI invia automaticamente una notifica via email al termine di un job di valutazione del modello.
Visualizzare le metriche di valutazione
Console
Nella console Google Cloud , vai alla pagina Modelli di Vertex AI.
Per visualizzare il flusso di lavoro di valutazione del modello dell'API Vertex AI in
Vertex AI Pipelines, consulta i notebook di esempio per i seguenti tipi di
modello:
L'SDK per la valutazione dei modelli con Vertex AI è in
versione sperimentale. Per registrarti alla versione sperimentale, compila il
modulo di onboarding.
Confrontare le metriche di valutazione
Puoi confrontare i risultati della valutazione di diversi modelli, versioni dei modelli e
job di valutazione. Per ulteriori informazioni sul controllo delle versioni del modello, vedi Controllo delle versioni in
Model Registry.
Puoi confrontare solo modelli dello stesso tipo, ad esempio classificazione,
regressione o previsione. Quando confronti modelli diversi, tutte le
versioni del modello devono essere dello stesso tipo.
Puoi confrontare solo 5 o meno valutazioni alla volta.
Vai a Vertex AI Model Registry nella console Google Cloud :
[[["Facile da capire","easyToUnderstand","thumb-up"],["Il problema è stato risolto","solvedMyProblem","thumb-up"],["Altra","otherUp","thumb-up"]],[["Difficile da capire","hardToUnderstand","thumb-down"],["Informazioni o codice di esempio errati","incorrectInformationOrSampleCode","thumb-down"],["Mancano le informazioni o gli esempi di cui ho bisogno","missingTheInformationSamplesINeed","thumb-down"],["Problema di traduzione","translationIssue","thumb-down"],["Altra","otherDown","thumb-down"]],["Ultimo aggiornamento 2025-09-04 UTC."],[],[],null,["# Evaluate models using Vertex AI\n\nThis page describes how to evaluate models using Vertex AI. For\nan overview, see [model evaluation in Vertex AI](/vertex-ai/docs/evaluation/introduction).\n\nPrerequisites\n-------------\n\n1. Follow the steps at [Set up a project and a development environment](/vertex-ai/docs/start/cloud-environment).\n In addition, enable the following services:\n\n - [Compute Engine API](https://console.cloud.google.com/flows/enableapi?apiid=compute.googleapis.com)\n - [Dataflow API](https://console.cloud.google.com/flows/enableapi?apiid=dataflow.googleapis.com)\n2. Vertex AI can evaluate models that are trained either\n through AutoML or custom training. For the Google Cloud console\n guide, you should have a trained model [imported to\n Vertex AI Model Registry](/vertex-ai/docs/model-registry/import-model).\n\n3. Upload your test dataset to [BigQuery](/bigquery/docs/loading-data) or\n [Cloud Storage](/storage/docs/uploading-objects). The test dataset should contain the ground\n truth, which is the actual result expected for an inference. Obtain the link\n to the file or the dataset ID.\n\n4. Have a [batch inference output](/vertex-ai/docs/predictions/batch-predictions) in the form of a\n BigQuery table or Cloud Storage URI.\n\n5. Make sure your [default Compute Engine service account](/iam/docs/service-account-types#default) has the\n following [IAM permissions](/vertex-ai/docs/general/iam-permissions):\n\n - Vertex AI Administrator (`aiplatform.admin`)\n - Vertex AI Service Agent (`aiplatform.serviceAgent`)\n - Storage Object Admin (`storage.objectAdmin`)\n - Dataflow Worker (`dataflow.worker`)\n - BigQuery Data Editor (`bigquery.dataEditor`) (only required if you are providing data in the form of BigQuery tables)\n\nCreate an evaluation\n--------------------\n\n### Console\n\n1. In the Google Cloud console, go to the Vertex AI Models page.\n\n [Go to the Models page](https://console.cloud.google.com/vertex-ai/models)\n2. Click the name of the model you want to evaluate.\n\n3. Click the version number for the model.\n\n4. On the **Evaluate** tab, click **Create Evaluation**.\n\n5. Enter an **Evaluation name**.\n\n6. Select an **Objective**, such as classification or regression.\n\n7. Enter the **Evaluation target column name**, which is the column from the\n training data that the model is trained to predict.\n\n8. For **Select source**, select the source for your test dataset.\n\n 1. For **BigQuery table** , enter the **BigQuery path**.\n\n 2. For **File on Cloud Storage** , enter the **Cloud Storage path**.\n\n9. For **Batch prediction output**, select an output format.\n\n 1. Enter the BigQuery path or Cloud Storage URI.\n10. Click **Start Evaluation**.\n\n### Python\n\nTo view the Vertex AI API model evaluation workflow in\nVertex AI Pipelines, see the example notebooks for the following model\ntypes:\n\n- [AutoML tabular classification](https://colab.sandbox.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/automl_tabular_classification_model_evaluation.ipynb)\n\n- [AutoML tabular regression](https://colab.sandbox.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/automl_tabular_regression_model_evaluation.ipynb)\n\n- [AutoML video classification](https://colab.sandbox.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/automl_video_classification_model_evaluation.ipynb)\n\n- [Custom tabular classification](https://colab.sandbox.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/custom_tabular_classification_model_evaluation.ipynb)\n\n- [Custom tabular regression](https://colab.sandbox.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/custom_tabular_regression_model_evaluation.ipynb)\n\n### Python SDK\n\nThe SDK for evaluating models with Vertex AI is in\nExperimental. To sign up for the Experimental, fill out the\n[onboarding form](https://docs.google.com/forms/d/159DJxDx8cQpsjwsNkS7j-qCwsz2uTDVwVQPv4ZfWM50/viewform?edit_requested=true).\n\nVertex AI automatically sends an email notification when\na model evaluation job is complete.\n\nView evaluation metrics\n-----------------------\n\n**Note:** For [BigQuery ML models](/bigquery/docs/model_eval) that are registered to Model Registry, Vertex AI only shows evaluation metrics for regression and binary classification models. \n\n### Console\n\n1. In the Google Cloud console, go to the Vertex AI Models page.\n\n [Go to the Models page](https://console.cloud.google.com/vertex-ai/models)\n2. Navigate to the model version.\n\n3. View metrics in the **Evaluate** tab.\n\n### Python\n\nTo view the Vertex AI API model evaluation workflow in\nVertex AI Pipelines, see the example notebooks for the following model\ntypes:\n\n- [AutoML tabular classification](https://colab.sandbox.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/automl_tabular_classification_model_evaluation.ipynb)\n\n- [AutoML tabular regression](https://colab.sandbox.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/automl_tabular_regression_model_evaluation.ipynb)\n\n- [AutoML video classification](https://colab.sandbox.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/automl_video_classification_model_evaluation.ipynb)\n\n- [Custom tabular classification](https://colab.sandbox.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/custom_tabular_classification_model_evaluation.ipynb)\n\n- [Custom tabular regression](https://colab.sandbox.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/custom_tabular_regression_model_evaluation.ipynb)\n\n### Python SDK\n\nThe SDK for evaluating models with Vertex AI is in\nExperimental. To sign up for the Experimental, fill out the\n[onboarding form](https://docs.google.com/forms/d/159DJxDx8cQpsjwsNkS7j-qCwsz2uTDVwVQPv4ZfWM50/viewform?edit_requested=true).\n\nCompare evaluation metrics\n--------------------------\n\nYou can compare evaluation results across different models, model versions, and\nevaluation jobs. For more information about model versioning, see [Versioning in\nModel Registry](/vertex-ai/docs/model-registry/versioning).\n\nYou can only compare models of the same type, such as classification,\nregression, or forecasting. When comparing different models, all the\nmodel versions must be the same type.\n\nYou can only compare 5 or fewer evaluations at a time.\n\n1. Go to the Vertex AI Model Registry in the Google Cloud console:\n\n [Go to the Models page](https://console.cloud.google.com/vertex-ai/models)\n2. Navigate to your model or model version:\n\n - To compare across different models on the **Models** page, select the\n checkboxes next to the names of the models you want to compare.\n\n - To compare across different model versions:\n\n 1. Click the name of your model on the **Models** page to open the list\n of model versions.\n\n 2. Select the checkboxes next to the versions you want to compare.\n\n - To compare across evaluation jobs for the same model version:\n\n 1. Click the name of your model on the **Models** page to open the list\n of model versions.\n\n 2. Click the version number.\n\n 3. Select the checkboxes next to the evaluation jobs you want to compare.\n\n3. Click **Compare**.\n\nWhat's next\n-----------\n\n- Learn how to [iterate on your model](/vertex-ai/docs/training/evaluating-automl-models#iterate)."]]
