Vertex AI dapat mengevaluasi model yang dilatih
melalui AutoML atau pelatihan kustom. Untuk mengetahui panduan konsol, Anda harus memiliki model terlatih yang diimpor ke
Vertex AI Model Registry. Google Cloud
Upload set data pengujian Anda ke BigQuery atau
Cloud Storage. Set data pengujian harus berisi kebenaran dasar,
yang merupakan hasil sebenarnya yang diharapkan untuk sebuah prediksi. Dapatkan link ke
file atau ID set data.
SDK untuk mengevaluasi model dengan Vertex AI masih dalam
versi Eksperimental. Untuk mendaftar ke versi Eksperimental, isi
formulir aktivasi.
Membandingkan metrik evaluasi
Anda dapat membandingkan hasil evaluasi antara berbagai model, versi model, dan
tugas evaluasi. Untuk mengetahui informasi selengkapnya tentang pembuatan versi model, lihat Pembuatan versi di
Model Registry.
Anda hanya dapat membandingkan model dari jenis yang sama, seperti klasifikasi,
regresi, atau perkiraan. Saat membandingkan model yang berbeda, semua
versi model harus berjenis sama.
Anda hanya dapat membandingkan 5 evaluasi atau kurang dari jumlah itu sekaligus.
Buka Vertex AI Model Registry di konsol Google Cloud :
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 2025-08-18 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)."]]
