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Snowflake&body=${pageURL}`;\r\nwindow.open(shareURL);\r\n}\r\n\r\n\r\n\r\n\r\n\r\n});","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"}},":itemsOrder":["markup_editor"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"}},":itemsOrder":["container_949147658"]},"cq:LiveSyncConfig":{"cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/ko/site/share-icons/share-icons",":type":"cq:LiveCopy"}},":itemsOrder":["root","cq:LiveSyncConfig"]}},":itemsOrder":["text","experiencefragment"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"fundamentals-main-content",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"container":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn 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MLOps는 ML 이니셔티브가 원활하고 효율적으로 운영되도록 보장하는 핵심 요소이며, 궁극적으로 신뢰할 수 있는 결과를 보장합니다. \u003C/p\u003E\r\n\u003Cp\u003E효과적인 MLOps는 배포 시간을 단축하고, 생산성을 최적화하며, 복원력 있는 워크플로우를 구축합니다. 조직은 반복적인 작업을 자동화함으로써 가동 중지 시간을 줄이고 개발 주기를 최적화하며, 리소스를 더욱 전략적인 프로젝트에 집중할 수 있습니다. MLOps는 효율성뿐만 아니라 탁월한 모델 성능을 보장합니다. 지속적 통합 및 지속적 배포(CI/CD)와 같은 방식을 채택하면 팀은 자동화된 테스트, 모니터링 및 재훈련 프로세스를 워크플로우에 통합할 수 있습니다. 이를 통해 기업은 모델을 지속적으로 개선하여 예측을 정확하게 유지하고, 실사용데이터(RWD)에 맞춰 모델을 조정할 수 있습니다. 결과적으로 조직은 비즈니스 요구 사항에 맞춰 진화하는 더욱 스마트하고 적응력이 뛰어난 시스템을 구축할 수 있습니다. \u003C/p\u003E\r\n\u003Cp\u003E조직은 MLOps 도입을 통해 모델 개발부터 배포에 이르는 일련의 과정에서 ML 이니셔티브의 잠재력을 최대한 활용할 수 있습니다. MLOps는 향상된 효율성과 모델 정확도, 그리고 확장된 협업을 통해, 조직이 혁신을 주도하고 실질적인 비즈니스 성과를 달성할 수 있도록 지원합니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy":{"columnClassNames":{"text_copy_copy":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12","text_copy_860073138":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"key-benefits",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-37ea78b81c","type":"heading3","lines":["MLOps의 주요 이점"],":type":"snowflake-site/components/title-v2"},"text_copy_860073138":{"id":"text-01a0b9f635","text":"\u003Cp\u003EMLOps는 프로토타입 제작부터 프로덕션까지의 워크플로우를 가속화하려는 조직에 여러 핵심 이점을 제공합니다. 그 중 몇 가지를 살펴보겠습니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy":{"id":"title-v2-a22b3b80dd","type":"heading4","lines":["1. 더 빠른 개발과 배포를 위한 ML 프로세스 자동화"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text":{"id":"text-1dbd5ce4bb","text":"\u003Cp\u003EMLOps는 데이터 전처리, 모델 학습, 추론 등 ML 수명 주기의 주요 단계에 엔드투엔드 자동화를 구현합니다. 이와 같은 프로세스 자동화를 통해 조직은 ML 모델을 프로덕션에 적용하는 데 드는 시간과 노력을 크게 줄일 수 있습니다. 기업은 가속화된 개발 및 배포 주기를 통해, 오늘날 급변하는 시장에서 새로운 기회를 포착하고 앞서 나갈 수 있습니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy":{"id":"title-v2-ec5ce682d7","type":"heading4","lines":["2. 데이터 사이언티스트, ML 엔지니어 및 IT 운영 간의 협업 강화"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy":{"id":"text-689c30c540","text":"\u003Cp\u003E데이터 사이언티스트, ML 엔지니어링, IT 운영 간의 격차를 해소하는 것이 MLOps의 핵심 강점 중 하나입니다. MLOps는 사일로를 해소하고 원활한 커뮤니케이션 채널을 구축하여, 데이터 사이언티스트, ML 엔지니어 및 IT 전문가가 프로덕션 환경에서 협업하며 모델을 배포, 모니터링 및 관리할 수 있도록 지원합니다. 이러한 협업을 통해 운영 병목 현상을 해소하고 생산성을 강화하며, 실제 애플리케이션에 맞게 ML 모델을 최적화할 수 있습니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c":{"id":"title-v2-0291bb21ec","additionalClasses":"what-is-rag","type":"heading4","lines":["3. ML 모델의 확장성 및 신뢰성 향상"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_copy":{"id":"text-7efd6a95f9","text":"\u003Cp\u003EMLOps는 ML 이니셔티브를 효과적으로 확장하는 데 필요한 도구와 프레임워크를 제공합니다. 이를 통해 팀은 성능과 신뢰성을 유지하면서 다양한 환경에서 ML 모델의 배포를 관리할 수 있습니다. 또한 MLOps는 ML 모델의 선제적 모니터링 및 관리를 지원하여, 조직은 비즈니스 운영에 영향을 미칠 수 있는 잠재적인 문제를 사전에 식별하고 해결할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003EMLOps를 도입함으로써 조직은 ML 이니셔티브의 잠재력을 최대한 발휘하고, 혁신과 효율성을 강화하며 경쟁 우위를 확보할 수 있습니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_copy_860073138","title_v2_copy","text","title_v2_copy_copy","text_copy","title_v2_copy_copy_c","text_copy_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_295276270":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"difference",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-ac143138ee","type":"heading3","lines":["MLOps와 DevOps의 차이점"],":type":"snowflake-site/components/title-v2"},"text_copy_copy_295276270":{"id":"text-aba0f99b75","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EMLOps와 DevOps는 모두 소프트웨어 개발 및 배포를 간소화하는 데 중요한 방법론이나, 각각 고유한 중점 분야와 목표가 있습니다.\u003C/p\u003E\n\u003Cp\u003EDevOps는 기존 소프트웨어 시스템의 신속한 개발 및 배포에 중점을 두고, 개발 팀과 운영 팀을 통합하여 CI/CD 파이프라인, 테스트 및 배포와 같은 프로세스를 간소화하는 협업을 촉진하는 것을 목표로 합니다. 반면, ML은 기존 DevOps로는 해결할 수 없는 복잡성을 초래하는데, 이는 특히 ML 모델을 프로덕션 환경에 배포할 때 두드러집니다.\u003C/p\u003E\n\u003Cp\u003E바로 여기서 MLOps가 중요한 역할을 합니다. MLOps는 DevOps 원칙을 기반으로 이를 확장하여 머신러닝 프로젝트의 수명 주기를 관리합니다. MLOps는 코딩과 배포뿐만 아니라 데이터 전처리, 피처 엔지니어링, 모델 학습, 추론 중 ML 모델 모니터링 등의 워크플로우도 포괄하고 있습니다. 즉, MLOps는 머신러닝의 실험과 프로덕션 간의 간극을 메운다고 할 수 있습니다. \u003C/p\u003E\n\u003Cp\u003EDevOps에서 MLOps로 전환 시 염두에 두어야 할 몇 가지 주요 고려 사항은 다음과 같습니다. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003E지속적인 모니터링 및 최적화: \u003C/b\u003E기존 소프트웨어와 달리 ML 모델은 지속적인 관리가 필요합니다. 데이터 분산의 변화(데이터 드리프트)나 성능 저하가 발생함에 따라 모니터링 및 옵저버빌리티 시스템은 필수적입니다. 자동화된 경고는 조직이 이러한 문제를 선제적으로 해결하는 데 도움이 될 수 있습니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E엔드투엔드 자동화: \u003C/b\u003EMLOps는 ML 워크플로우를 간소화하는 데 매우 중요합니다. 데이터 수집, 피처 엔지니어링, 모델 학습, 프로덕션 환경 배포와 같은 프로세스를 자동화하면 더 쉽게 일관된 성능을 유지할 수 있습니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E재현성 및 버전 관리:\u003C/b\u003E 재현성은 ML 워크플로우의 규정 준수 및 디버깅 측면에서 매우 중요합니다. 버전 관리는 모델과 데이터 세트를 다양한 환경에서 쉽게 재현하고 일관되게 배포할 수 있도록 지원하여 확장성과 신뢰성을 보장합니다.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EMLOps를 도입하는 조직은 고도화된 ML 워크플로우와 DevOps의 전략적 효율성을 함께 활용하여 더욱 강력한 경쟁 우위를 확보할 수 있습니다. \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_copy_copy_295276270"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_1790538746":{"columnClassNames":{"container_copy_43816":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"basic",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-ff9fc752cd","type":"heading3","lines":["MLOps의 기본 구성 요소"],":type":"snowflake-site/components/title-v2"},"container_copy_43816":{"columnClassNames":{"text_copy_copy_29527":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"how-rag-works",":type":"snowflake-site/components/container",":items":{"text_copy_copy_29527":{"id":"text-a05a335eae","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003E MLOps는 엔드투엔드 ML 워크플로우 전반에 걸쳐 원활한 협업과 운영 우수성을 보장하는 몇 가지 핵심 구성 요소로 이루어집니다. MLOps의 기본 구성 요소는 다음과 같습니다. \u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003E데이터 버전 관리 및 실험 재현성:\u003C/b\u003E ML 실험 과정에서, 모델에 사용된 데이터 세트를 추적하고 데이터 세트 버전을 관리하는 능력은 성공을 거두는 데 매우 중요합니다. MLOps 플랫폼은 데이터 버전 관리 기능을 제공하여 데이터 사이언티스트가 데이터 세트의 변경 사항을 쉽게 관리하고 추적할 수 있도록 지원합니다. 이를 통해 재현성을 확보하고 더 손쉽게 실험을 반복하며, 결과를 비교하여 지속적인 개선을 이룰 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EML을 위한 CI/CD:\u003C/b\u003E 소프트웨어 개발에서 흔히 사용하는 CI/CD 방식은 ML에서도 마찬가지로 중요합니다. MLOps 플랫폼은 ML 모델의 구축, 테스트 및 배포를 자동화하는 도구를 제공합니다. 데이터 사이언티스트는 CI/CD 기능을 통해 모델을 프로덕션 환경에 원활하게 통합하여 수작업을 줄이고, 시장 출시 시간을 단축할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003E모델 모니터링 및 거버넌스:\u003C/b\u003E 프로덕션 단계에서 ML 모델이 배포된 후에는, 성능을 모니터링하며 정확하고 신뢰할 수 있는 예측을 지속적으로 제공해야 합니다. MLOps 플랫폼은 주요 성능 지표를 추적하고 실시간으로 이상을 감지하는 모니터링 및 거버넌스 기능을 제공합니다. 버전 관리, 규정 준수 추적 및 감사를 지원하는 거버넌스 기능은 끊임없이 변화하는 규정을 준수하고 신뢰와 투명성을 유지하는 데 필수적입니다. \u003C/p\u003E\n\u003Cp\u003E조직은 MLOps가 가진 이러한 기본 구성 요소를 활용함으로써 ML 워크플로우를 효과적으로 관리하고, 데이터 사이언티스트, ML 엔지니어링 및 IT 팀 간의 협업을 개선하며, ML 모델의 대규모 배포 및 유지 관리에 있어 더 큰 성과를 달성할 수 있습니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["text_copy_copy_29527"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"}},":itemsOrder":["title_v2","container_copy_43816"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"container_copy_copy_445119867":{"columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 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