이 페이지에서는 Vertex AI Workbench 관리형 노트북에서 머신러닝(ML) 모델을 개발하는 일반적인 방법을 설명합니다. ML 모델 개발, Vertex AI 커스텀 학습, BigQuery ML에 일반적으로 사용되는 사전 설치된 Python 패키지를 사용할 수 있습니다.
일반적인 Python 패키지
기본적으로 관리형 노트북 인스턴스에는 모델 개발에 일반적으로 사용되는 Python 패키지가 사전 설치되어 있습니다.
이 패키지를 노트북 파일로 가져오면 사용할 수 있습니다.
BigQuery ML을 사용하면 관리형 노트북 인스턴스 내에서 BigQuery 데이터를 사용하는 모델을 학습시킬 수 있습니다.
예를 들어 BigQuery용 Python 클라이언트를 사용하여 노트북 파일에서 SQL 명령어를 전송하여 모델을 만든 다음 모델을 사용하여 일괄 예측을 가져올 수 있습니다.
BigQuery ML은 BigQuery 연산 엔진을 활용하므로 일괄 예측이나 모델 학습에 필요한 컴퓨팅 리소스를 배포할 필요가 없습니다.
그러면 학습, 평가, 예측을 설정하는 데 걸리는 시간을 줄일 수 있습니다.
[[["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: 2025-07-09(UTC)"],[],[],null,["# Model development in Vertex AI Workbench managed notebooks\n\nModel development in a managed notebooks instance\n=================================================\n\n\n| Vertex AI Workbench managed notebooks is\n| [deprecated](/vertex-ai/docs/deprecations). On\n| April 14, 2025, support for\n| managed notebooks will end and the ability to create managed notebooks instances\n| will be removed. Existing instances will continue to function\n| but patches, updates, and upgrades won't be available. To continue using\n| Vertex AI Workbench, we recommend that you\n| [migrate\n| your managed notebooks instances to Vertex AI Workbench instances](/vertex-ai/docs/workbench/managed/migrate-to-instances).\n\n\u003cbr /\u003e\n\nThis page describes common ways to develop a machine learning (ML) model\nin Vertex AI Workbench managed notebooks. You can use\npre-installed Python packages that are commonly used for ML model development,\nVertex AI custom training, and BigQuery ML.\n\n### Common Python packages\n\nBy default, managed notebooks instances are pre-installed\nwith Python packages that are commonly used for model development.\nImport these packages into your notebook file and they are ready to use.\n\n### Vertex AI custom training\n\nYou can use [Vertex AI custom training](/vertex-ai/docs/training/overview)\nto create and train models from within\nyour managed notebooks instance.\n\n[Install one of the Vertex AI\nclient libraries](/vertex-ai/docs/start/client-libraries) on your\ninstance, or use the [Vertex AI API](/vertex-ai/docs/reference/rest)\nto send API requests from a Jupyter notebook file.\n\n### BigQuery ML\n\nUsing [BigQuery ML](/bigquery-ml/docs), you can train models that use\nyour BigQuery data, all from within\nyour managed notebooks instance.\nFor example, by using the [Python client for\nBigQuery](/python/docs/reference/bigquery/latest),\nyou can send SQL commands from your notebook file\nto create a model, and then use the model to get batch predictions.\n\nBigQuery ML leverages the BigQuery computational engine,\nso you don't need to deploy the compute resources\nrequired for batch predictions or model training.\nThis can reduce the time it takes to set up training, evaluation,\nand prediction.\n\nWhat's next\n-----------\n\n- To learn more about Vertex AI custom training, see\n [Understand the custom training service](/vertex-ai/docs/training/understanding-training-service).\n\n- To learn more about BigQuery ML, see\n [What is BigQuery ML?](/bigquery-ml/docs/introduction)"]]
