如果您升级至少一个资源， Google Cloud 控制台将改用 Cloud Translation API，而不是 AutoML API。因此，在 Google Cloud 控制台中创建新数据集时，系统会默认创建原生数据集。此更改在项目级进行，因此项目的其他用户也会看到此更改。如需创建旧版数据集，您必须选择“创建旧版数据集”选项或使用 AutoML API。
在训练新的自定义模型时， Google Cloud 控制台会使用 AutoML API 或 Cloud Translation API，具体取决于数据集。对于旧版数据集，控制台使用 AutoML API 创建旧版模型。对于原生数据集， Google Cloud 控制台使用 Cloud Translation API 创建原生模型。
Cloud Translation API
如需通过 Cloud Translation API 管理原生资源，您需要更新代码，以使用正确的资源 ID 调用正确的 API。例如，如果您有调用 AutoML API 的命令并引用旧版资源 ID，则需要更新这些命令以调用 Cloud Translation API 并引用原生资源 ID。
[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["很难理解","hardToUnderstand","thumb-down"],["信息或示例代码不正确","incorrectInformationOrSampleCode","thumb-down"],["没有我需要的信息/示例","missingTheInformationSamplesINeed","thumb-down"],["翻译问题","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["最后更新时间 (UTC)：2025-09-04。"],[],[],null,["# Upgrade AutoML resources\n========================\n\nIf you have existing resources that were created by using the AutoML API,\nyou can upgrade those resources to manage them through the\nCloud Translation - Advanced API without any service interruptions or additional\ncosts. During the upgrade, Cloud Translation copies your AutoML\n(legacy) resources, such as datasets and models, and creates new\nCloud Translation (native) resources through the Cloud Translation API.\n\nWe recommend that you use Cloud Translation because future enhancements to\ndatasets and customs models will apply only to Cloud Translation. Upgraded\nresources can take advantage of those future enhancements such as additional\nlanguage pair support.\n\nThere's no requirement to upgrade your resources. You can still use the\nAutoML API, which will continue to be available.\n\nUpgrade considerations\n----------------------\n\nAfter upgrading, your native and legacy resources exist together but are managed\nby different APIs. To access and manage the upgraded resources, you must use the\nCloud Translation API, not the AutoML API.\n\nThe native resources are identical to legacy resources except for their resource\nIDs. Cloud Translation doesn't make any changes to legacy resources. You can\ncontinue to work with your legacy resources as before.\n\nYou can choose to upgrade some or all of your resources. When you upgrade a\ndataset, any models that are associated with that dataset are also automatically\nupgraded. Only models without an underlying dataset (like in cases where the\nassociated dataset was deleted) can be manually upgraded on their own.\n\n### Differences between legacy and native resources\n\nThe following table outlines the differences between legacy and native\nresources.\n\n### Google Cloud console behavior post upgrade\n\nIf you upgrade at least one resource, the Google Cloud console switches to\nusing the Cloud Translation API instead of the AutoML API. So, when you create\nnew datasets in the Google Cloud console, you create native datasets by\ndefault. This change happens at the project level, so other users of your\nproject also see this change. To create a legacy dataset, you must select the\ncreate legacy dataset option or use the AutoML API.\n\nWhen training new custom models, the Google Cloud console uses the\nAutoML API or Cloud Translation API, depending on the dataset. For legacy\ndatasets, the console uses the AutoML API to create legacy models. For\nnative datasets, the Google Cloud console uses the Cloud Translation API to\ncreate native models.\n\n### Cloud Translation API\n\nTo manage native resources through the Cloud Translation API, you need to update\nyour code to call the correct APIs with the correct resource IDs. For example,\nif you have commands that call the AutoML API and reference legacy resource\nIDs, you need to update those commands to call the Cloud Translation API and\nreference the native resource IDs.\n\nFor more information about the Cloud Translation API, see the\n[projects.locations.datasets](/translate/docs/reference/rest/v3/projects.locations.datasets) and\n[projects.locations.models](/translate/docs/reference/rest/v3/projects.locations.models) resources.\n\nUpgrade resources\n-----------------\n\nUse the Google Cloud console to upgrade existing AutoML resources to\nCloud Translation resources.\n\n1. Go to the Cloud Translation console.\n\n [Go to the\n Translation page](https://console.cloud.google.com/translation)\n2. Click **Datasets** to view your existing datasets.\n\n3. Click **Upgrade** to open the **Upgrade dataset** pane, which lists the\n datasets that you can upgrade.\n\n When you upgrade a dataset, any model that's associated with that dataset\n is also automatically upgraded.\n4. Select the datasets to upgrade, and then click **Start upgrading**.\n\n On the **Datasets** page, the Google Cloud console lists your upgraded and\n legacy datasets in separate tables.\n5. To manually upgrade models, in the navigation pane, click **Models** to view\n your existing models.\n\n You can manually upgrade only models without an underlying dataset (like if\n the model's associated dataset was deleted).\n6. Click **Upgrade** to open the **Upgrade model** pane.\n\n7. Select the models to upgrade, and the click **Start upgrading**.\n\n On the **Models** page, the Google Cloud console lists your upgraded and\n legacy models in separate tables.\n\nAfter you upgrade your resources, consider making the following changes:\n\n- Update existing code to use the Cloud Translation API and newly created resources. For more information, see [Create and manage datasets](/translate/docs/advanced/automl-datasets) and [Create and manage models](/translate/docs/advanced/automl-models).\n- For translation predictions, use the Cloud Translation API instead of the AutoML API. For more information, see [translating text with a custom\n model](/translate/docs/advanced/translating-text-v3).\n\nDelete legacy resources\n-----------------------\n\nAfter you have fully migrated to using the new resources and the\nCloud Translation API, you can remove your legacy resources so that you only have a\nsingle set of resources to work with.\n\n1. Go to the Cloud Translation console.\n\n [Go to the\n Translation page](https://console.cloud.google.com/translation)\n2. In the navigation pane, click **Datasets** to view legacy datasets.\n\n3. For each dataset in the **Legacy datasets** table, select more_vert **More \\\u003e Delete** and then click\n **Confirm**.\n\n4. In the navigation pane, click **Models** to view legacy models.\n\n5. For each model in the **Legacy models** table, select more_vert **More \\\u003e Delete** and then click\n **Confirm**."]]
