Vertex AI Model Registry は、ML モデルのライフサイクルを管理できる中央リポジトリです。Model Registry ではモデルの概要を確認できるため、新しいバージョンの整理、追跡、トレーニングの向上が実現します。デプロイするモデル バージョンがある場合は、レジストリから直接エンドポイントに割り当てるか、エイリアスを使用してモデルをエンドポイントにデプロイできます。
Vertex AI Model Registry は、カスタムモデルとすべての AutoML データ型(テキスト、表形式、画像、動画)をサポートしています。Model Registry では、BigQuery ML モデルもサポートされます。BigQuery ML でトレーニングしたモデルは、BigQuery ML からエクスポートしたり、Model Registry にインポートすることなく、Model Registry に登録できます。
モデル バージョンの詳細ページでは、評価、エンドポイントへのデプロイ、バッチ予測の設定、特定のモデルの詳細の表示を行うことができます。Vertex AI Model Registry は、最適なモデルを管理して本番環境にデプロイするための、シンプルで合理化されたインターフェースを提供します。
一般的なワークフロー
Model Registry の操作には、有効なワークフローが数多くあります。まず、次のガイドラインに沿って、Model Registry でできることと、モデルのトレーニングのどの段階にあるかを理解することをおすすめします。
[[["わかりやすい","easyToUnderstand","thumb-up"],["問題の解決に役立った","solvedMyProblem","thumb-up"],["その他","otherUp","thumb-up"]],[["わかりにくい","hardToUnderstand","thumb-down"],["情報またはサンプルコードが不正確","incorrectInformationOrSampleCode","thumb-down"],["必要な情報 / サンプルがない","missingTheInformationSamplesINeed","thumb-down"],["翻訳に関する問題","translationIssue","thumb-down"],["その他","otherDown","thumb-down"]],["最終更新日 2025-09-04 UTC。"],[],[],null,["# Introduction to Vertex AI Model Registry\n\n| To see an example of getting started with Vertex AI Model Registry,\n| run the \"Get started with Vertex AI Model Registry\" notebook in one of the following\n| environments:\n|\n| [Open in Colab](https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_registry/get_started_with_model_registry.ipynb)\n|\n|\n| \\|\n|\n| [Open in Colab Enterprise](https://console.cloud.google.com/vertex-ai/colab/import/https%3A%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fmodel_registry%2Fget_started_with_model_registry.ipynb)\n|\n|\n| \\|\n|\n| [Open\n| in Vertex AI Workbench](https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https%3A%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fmodel_registry%2Fget_started_with_model_registry.ipynb)\n|\n|\n| \\|\n|\n| [View on GitHub](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_registry/get_started_with_model_registry.ipynb)\n\nThe Vertex AI Model Registry is a central repository where you can manage\nthe lifecycle of your ML models. From the Model Registry,\nyou have an overview of your models so you can better organize, track,\nand train new versions. When you have a model version you would like to deploy,\nyou can assign it to an endpoint directly from the registry,\nor using aliases, deploy models to an endpoint.\n\nThe Vertex AI Model Registry supports custom models and all\nAutoML data types - tabular, image, and video. The\nModel Registry\ncan also support BigQuery ML models. If you have models trained in\nBigQuery ML, you can register them with the\nModel Registry without needing to export them from\nBigQuery ML or import them into the Model Registry.\n\nFrom the model version details page you can evaluate, deploy to an endpoint,\nset up batch inference, and view specific model details. The Vertex AI Model Registry\nprovides a straightforward and streamlined interface to manage and deploy your\nbest models to production.\n\nCommon workflow\n---------------\n\nThere are many valid workflows for working in the Model Registry.\nTo get started, you might want to follow these guidelines to understand what you can\ndo in the Model Registry and at what stage in your model-training journey.\n\n- Import models to the Model Registry.\n- Create new models, assign a model version the default alias, ready for production.\n- Add other aliases, or labels to help you manage and organize your models and model versions.\n- Deploy your models to an endpoint for online inference.\n- Run batch inference, and start your model evaluation pipeline.\n- View your model details and view performance metrics from the model details page.\n\nTo learn more about how to integrate your BigQuery ML models with\nVertex AI, see the\n[BigQuery ML documentation.](/bigquery-ml/docs/managing-models-vertex)\n\nSearch and discover models using Dataplex Universal Catalog\n-----------------------------------------------------------\n\nDataplex Universal Catalog is a platform for storing, managing, and accessing your\nmetadata. Dataplex Universal Catalog provides a way to search\nfor your Vertex AI models across projects and regions.\n\nFor more information, see [About data catalog management in\nDataplex Universal Catalog](/dataplex/docs/catalog-overview).\n\nWhat's next\n-----------\n\nTo get started using Vertex AI Model Registry, see:\n\n- [Import models to Vertex AI](/vertex-ai/docs/model-registry/import-model)\n- [Model versioning with Model Registry](/vertex-ai/docs/model-registry/versioning)\n- [How to use model version aliases](/vertex-ai/docs/model-registry/model-alias)\n- [BigQuery ML and Model Registry](/vertex-ai/docs/model-registry/model-registry-bqml)\n- [Copy a model in Vertex AI Model Registry](/vertex-ai/docs/model-registry/copy-model)"]]