이 페이지에서는 Vertex AI와 상호작용하는 데 사용할 수 있는 인터페이스와 이를 사용해야 하는 경우를 설명합니다. Vertex AI의 노트북 솔루션 중 하나와 함께 이러한 인터페이스를 사용할 수 있습니다.
일부 Vertex AI 작업은 특정 인터페이스를 통해서만 사용할 수 있으므로 워크플로 중에 인터페이스 간에 전환해야 할 수 있습니다.
예를 들어 Vertex AI Experiments에서는 API를 사용하여 실험 실행에 데이터를 로깅해야 하지만 결과는 콘솔에서 볼 수 있습니다.
콘솔
Google Cloud 콘솔은 머신 러닝 리소스를 작업하는 데 사용할 수 있는 그래픽 사용자 인터페이스입니다.
Google Cloud 콘솔에서 관리형 데이터 세트, 모델, 엔드포인트, 작업을 관리할 수 있습니다. 콘솔을 통해 Cloud Storage 및 BigQuery와 같은 다른 Google Cloud 서비스에 액세스할 수도 있습니다.
그래픽 사용자 인터페이스를 통해 Vertex AI 리소스와 시각화를 보고 관리하려면 Google Cloud 콘솔을 사용하세요.
[[["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,["# Interfaces for Vertex AI\n\nThis page describes the interfaces that you can use to interact with\nVertex AI and when you should use them. You can use these interfaces\nalong with one of Vertex AI's\n[notebook solutions](/vertex-ai/docs/workbench/notebook-solution).\n\nSome Vertex AI operations are only available through specific\ninterfaces, so you may need to switch between interfaces during your workflow.\nFor example, in Vertex AI Experiments, you must use the API to log data\nto an experiment run, but you can view the results in the console. \n\n### Console\n\nThe Google Cloud console is a graphical user interface that you can use to\nwork with your machine learning resources.\n\nIn the Google Cloud console, you can manage your ,\nmodels, endpoints, and jobs. You can also access other Google Cloud services,\nsuch as Cloud Storage and BigQuery, through the console.\n\nUse the Google Cloud console if you prefer to view and manage your\nVertex AI resources and visualizations through a graphical user\ninterface.\n\nFor more information, see the **Dashboard** page of the Vertex AI section:\n\n[Go to the Dashboard](https://console.cloud.google.com/vertex-ai/)\n\n### gcloud\n\nThe [Google Cloud command-line interface (CLI)](/sdk/gcloud) is a set of tools for\ncreating and managing Google Cloud resources using the `gcloud` command.\n\nUse the Google Cloud CLI when you want to manage your Vertex AI\nresources from the command line or through scripts and other automation.\n\nFor more information, see [Install the gcloud CLI](/sdk/docs/install) and the\n[`gcloud ai`](/sdk/gcloud/reference/ai) reference.\n\n### Terraform\n\nTerraform is an (IaC) tool that you can use to\nprovision the infrastructure, such as resources and permissions, for multiple\nGoogle Cloud services, including Vertex AI.\n\nYou can define the Vertex AI resources and permissions for your Google Cloud\nproject in a Terraform configuration file. You can then use Terraform to apply\nthe configuration to your project by creating new resources and updating\nexisting resources.\n\nUse Terraform if you want to standardize the infrastructure for Vertex AI\nresources in your Google Cloud project and update the existing Google Cloud\nproject infrastructure while fulfilling resource dependencies.\n\nTo get started, see [Terraform support for Vertex AI](/vertex-ai/docs/start/use-terraform-vertex-ai).\n\n### Python\n\nUse the [Vertex AI SDK for Python](/vertex-ai/docs/python-sdk/use-vertex-ai-python-sdk) to programmatically automate your\nVertex AI workflow.\n\nThe Vertex AI SDK for Python is similar to the Vertex AI Python client\nlibrary, except the SDK is higher-level and less granular. For more\ninformation, see the [Understand the SDK and client library\ndifferences](/vertex-ai/docs/python-sdk/use-vertex-ai-python-sdk#sdk-vs-client-library).\n\nTo get started, see [Install the Vertex AI SDK](/vertex-ai/docs/start/install-sdk).\n\n### Client libraries\n\nClient libraries use each supported language's natural conventions to call the\nVertex AI API and reduce boilerplate code that you have to write.\n\nThe following languages are supported for Vertex AI:\n\n- Python. The Vertex AI Python client library is installed when you\n install the [Vertex AI SDK for Python](/vertex-ai/docs/python-sdk/use-vertex-ai-python-sdk).\n\n- Java\n\n- Node.js\n\n- C#\n\n- Go\n\nFor more information, see [Install the Vertex AI client libraries](/vertex-ai/docs/start/client-libraries).\n\n### REST\n\nThe Vertex AI REST API provides RESTful services for managing jobs,\nmodels, and endpoints, and for making inferences with hosted models\non Google Cloud.\n\nUse the REST API if you need to use your own libraries to call the\nVertex AI API from your application.\n\nTo get started, see the [Vertex AI API REST reference](/vertex-ai/docs/reference/rest).\n\nWhat's next\n-----------\n\n- [Set up a project and a development environment](/vertex-ai/docs/start/cloud-environment).\n- [Choose a training method](/vertex-ai/docs/start/training-methods).\n- Tutorials for [Image](/vertex-ai/docs/tutorials/image-classification-automl/overview), [Tabular](/vertex-ai/docs/tutorials/tabular-automl/overview), and [Custom training](/vertex-ai/docs/tutorials/image-classification-custom/overview).\n- Learn [best practices for implementing custom-trained ML models on\n Vertex AI](/architecture/ml-on-gcp-best-practices)."]]
