앱을 빌드하고 배포한 후에는 Google Cloud 콘솔 또는 명령줄을 사용하여 이러한 앱을 관리할 수 있습니다.
애플리케이션 관리
앱 업데이트
배포 취소된 앱
구성요소 노드 추가 또는 삭제와 같이 앱을 수정합니다. Vertex AI Vision에서 변경사항을 자동으로 저장합니다.
배포된 앱
배포된 앱의 변경사항은 Vertex AI Vision 서버에 자동으로 저장되며 배포된 애플리케이션은 영향을 받지 않습니다. 수정사항을 삭제하려면 변경사항 삭제 버튼을 클릭합니다. 변경사항을 저장하려면 애플리케이션 업데이트를 선택합니다. 이러한 로컬 변경사항을 애플리케이션에 적용하려면 현재 애플리케이션을 배포 취소한 후 다시 배포하세요.
[[["이해하기 쉬움","easyToUnderstand","thumb-up"],["문제가 해결됨","solvedMyProblem","thumb-up"],["기타","otherUp","thumb-up"]],[["이해하기 어려움","hardToUnderstand","thumb-down"],["잘못된 정보 또는 샘플 코드","incorrectInformationOrSampleCode","thumb-down"],["필요한 정보/샘플이 없음","missingTheInformationSamplesINeed","thumb-down"],["번역 문제","translationIssue","thumb-down"],["기타","otherDown","thumb-down"]],["최종 업데이트: 2025-09-04(UTC)"],[],[],null,["# Manage applications\n\nAfter you [build](/vision-ai/docs/build-app) and [deploy](/vision-ai/docs/deploy-app) apps, you can\nmanage these apps using the Google Cloud console or command line.\n\nManage an application\n---------------------\n\nUpdate an app\n-------------\n\n#### Undeployed apps\n\nMake any modifications to the app (such as adding or removing component nodes)\n; Vertex AI Vision will automatically store the changes.\n\n#### Deployed apps\n\nChanges to deployed apps are automatically stored to the Vertex AI Vision server\nand the deployed application is not affected. To discard modifications, click\nthe **Discard changes** button. To save changes, select\n**Update the application**. To apply these local changes to the application,\nundeploy the current application, then redeploy it.\n\nList apps\n---------\n\n### REST\n\nTo list all apps in a project, send a GET request by using the\n[projects.locations.applications.list](/vision-ai/docs/reference/rest/v1/projects.locations.applications/list)\nmethod.\n\n\nBefore using any of the request data,\nmake the following replacements:\n\n- \u003cvar translate=\"no\"\u003ePROJECT\u003c/var\u003e: Your Google Cloud [project ID or\n project number](/resource-manager/docs/creating-managing-projects#identifying_projects).\n- \u003cvar translate=\"no\"\u003eLOCATION_ID\u003c/var\u003e: The [region](/about/locations) where you are using Vertex AI Vision. For example: `us-central1`, `europe-west4`. See [available regions](/vision-ai/docs/warehouse-supported-regions).\n\n\nHTTP method and URL:\n\n```\nGET https://visionai.googleapis.com/v1/projects/PROJECT_NUMBER/locations/LOCATION_ID/applications\n```\n\nTo send your request, choose one of these options: \n\n#### curl\n\n| **Note:** The following command assumes that you have logged in to the `gcloud` CLI with your user account by running [`gcloud init`](/sdk/gcloud/reference/init) or [`gcloud auth login`](/sdk/gcloud/reference/auth/login) , or by using [Cloud Shell](/shell/docs), which automatically logs you into the `gcloud` CLI . You can check the currently active account by running [`gcloud auth list`](/sdk/gcloud/reference/auth/list).\n\n\nExecute the following command:\n\n```\ncurl -X GET \\\n -H \"Authorization: Bearer $(gcloud auth print-access-token)\" \\\n \"https://visionai.googleapis.com/v1/projects/PROJECT_NUMBER/locations/LOCATION_ID/applications\"\n```\n\n#### PowerShell\n\n| **Note:** The following command assumes that you have logged in to the `gcloud` CLI with your user account by running [`gcloud init`](/sdk/gcloud/reference/init) or [`gcloud auth login`](/sdk/gcloud/reference/auth/login) . You can check the currently active account by running [`gcloud auth list`](/sdk/gcloud/reference/auth/list).\n\n\nExecute the following command:\n\n```\n$cred = gcloud auth print-access-token\n$headers = @{ \"Authorization\" = \"Bearer $cred\" }\n\nInvoke-WebRequest `\n -Method GET `\n -Headers $headers `\n -Uri \"https://visionai.googleapis.com/v1/projects/PROJECT_NUMBER/locations/LOCATION_ID/applications\" | Select-Object -Expand Content\n```\n\nYou should receive a JSON response similar to the following:\n\n```\n{\n \"applications\": [\n {\n \"name\": \"projects/PROJECT_NUMBER/locations/LOCATION_ID/applications/test-application-1\",\n \"createTime\": \"2022-03-01T20:04:12.558371402Z\",\n \"updateTime\": \"2022-03-01T20:07:21.589713094Z\",\n \"displayName\": \"Test Application 1\",\n \"runtimeInfo\": {\n \"deployTime\": \"2022-03-01T20:07:21.460654Z\"\n }\n \"state\": \"DEPLOYED\"\n },\n {\n \"name\": \"projects/PROJECT_NUMBER/locations/LOCATION_ID/applications/test-application-2\",\n \"createTime\": \"2022-03-01T20:04:12.558371402Z\",\n \"updateTime\": \"2022-03-01T20:07:21.589713094Z\",\n \"displayName\": \"Test Application 2\",\n \"runtimeInfo\": {\n \"deployTime\": \"2022-03-01T20:07:21.460654Z\"\n }\n \"state\": \"DEPLOYED\"\n },\n ]\n}\n```\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\nWhat's next\n-----------\n\n- Learn how to read app input data from an ingestion stream or analyzed model output data in [Read stream data](/vision-ai/docs/read-stream)."]]
