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href=\"https://www.snowflake.com/ko/product/ai/\"\u003EAI 이니셔티브\u003C/a\u003E를 가속화하고 실질적인 활용 사례를 확대하도록 지원합니다.\u003C/p\u003E\r\n\u003Cp\u003E이 글에서는 자기 지도 학습의 고유한 특징과, 다양한 산업에서 새로운 AI 적용 사례를 지원하는 데 있어 그 중요성이 점점 커지는 이유를 살펴봅니다.\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":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"is",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-a2564fdeb8","additionalClasses":"headline-decoration","type":"heading2","lines":["자기 지도 학습이란?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-c826f5d9ed","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003E자기 지도 학습은 모델이 레이블이 없는 데이터에서 학습할 수 있도록 하는 \u003Ca href=\"https://www.snowflake.com/ko/product/features/end-to-end-ml-workflows/\"\u003E머신러닝\u003C/a\u003E(ML)의 한 형태입니다. 이는 지도 학습과 비지도 학습의 요소를 모두 결합하지만, 다음과 같은 차이가 있습니다.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E지도 학습\u003C/b\u003E은 모든 데이터에 사람이 레이블을 붙인 데이터 세트를 기반으로 합니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E비지도 학습\u003C/b\u003E은 가공 전 데이터에서 숨겨진 패턴이나 클러스터를 찾습니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E자기 지도 학습\u003C/b\u003E은 데이터의 구조로부터 자체적으로 의사 레이블 또는 학습 신호를 생성합니다. \u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003E자기 지도 학습은 자체적으로 생성한 신호를 활용함으로써 사람이 대규모의 수작업을 통해 레이블링을 수행하지 않아도 모델이 유용한 표현을 학습하도록 합니다. 이는 복잡한 \u003Ca href=\"https://www.snowflake.com/ko/customers/\"\u003E실제 과제\u003C/a\u003E에 적응할 수 있는 \u003Ca href=\"https://www.snowflake.com/ko/product/features/cortex/\"\u003EAI 시스템을 구축\u003C/a\u003E하는 데 실용적이고 확장 가능한 접근 방식입니다.\u003C/p\u003E\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_2060034519":{"columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"work",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-a0ede17102","additionalClasses":"headline-decoration","type":"heading2","lines":["자기 지도 학습의 작동 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구성됩니다.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E프리텍스트 태스크\u003C/b\u003E는 데이터 자체에서 설계한 인위적 학습 과제로, 이를 해결하는 과정에서 모델은 데이터의 의미 있는 구조를 포착하여 학습합니다. 해당 예로는 다음을 들 수 있습니다.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E자연어 처리에서는 모델이 문장에서 누락된 단어를 예측합니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E컴퓨터 비전에서는 이미지의 회전 여부를 판단하거나 누락된 픽셀을 복원합니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E음성 인식에서는 모델이 두 개의 오디오 샘플이 같은 화자의 것인지 식별합니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003E이러한 태스크는 수작업 레이블이 필요 없기 때문에, 일반적으로 어노테이션 비용이나 시간이 많이 들어 사용하기 어려운 대규모 데이터 세트를 활용해 모델이 학습할 수 있도록 합니다.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E다운스트림 태스크\u003C/b\u003E는 텍스트 분류, 이미지 인식 또는 음성 텍스트 변환 같은 실제 머신러닝 애플리케이션입니다. 모델이 프리텍스트 태스크로 사전 학습되면 학습된 표현이 다운스트림 태스크로 전이되며, 일반적으로 최소한의 파인튜닝만으로도 활용할 수 있습니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 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aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"rel",":type":"snowflake-site/components/container",":items":{"container_copy_copy":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"use",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-efd9142082","additionalClasses":"headline-decoration","type":"heading2","lines":["자기 지도 학습 vs. 지도 학습 및 비지도 학습"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-4c8d6fcf10","text":"\u003Ch3\u003E자기 지도 학습 vs. 지도 학습\u003C/h3\u003E\r\n\u003Cp\u003E지도 학습은 각 입력 데이터가 정답 출력이 대응되는 대규모 레이블 데이터 세트를 필요로 합니다. 예를 들어 이미지 분류 모델은 모든 이미지에 ‘고양이’ 또는 ‘개’와 같은 레이블이 붙어 있는 데이터 세트로 학습됩니다. 이 레이블은 명확한 학습 신호를 제공하지만, 대규모로 생성하려면 비용과 시간이 많이 듭니다. ​그럼에도 불구하고 지도 학습은 의료 진단이나 금융 사기 감지와 같이 오류 비용이 매우 높은 고정밀 작업에서 여전히 효과적이고 선호되는 방식입니다.\u003C/p\u003E\r\n\u003Cp\u003E자기 지도 학습은 수작업 레이블의 필요성을 제거합니다. 누락된 단어 예측이나 이미지 회전 예측과 같은 프리텍스트 태스크를 통해 가공 전 데이터로부터 직접 의사 레이블을 생성합니다. 이를 통해 모델은 방대한 규모의 레이블이 없는 데이터에서 자동으로 학습할 수 있으며, 지도 학습보다 훨씬 빠르고 자원 효율성이 높습니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E자기 지도 학습 vs. 비지도 학습\u003C/h3\u003E\r\n\u003Cp\u003E비지도 학습 역시 레이블이 없는 데이터에 의존하지만, 학습 신호는 다릅니다. 비지도 학습에서는 모델이 보통 데이터를 그룹화하거나 축소하며, 예를 들어 고객을 여러 세그먼트로 클러스터링하거나 데이터를 더 적은 차원으로 압축합니다. 이러한 방법은 패턴을 발견하는 데는 유용하지만, 다른 태스크로 전이 가능한 표현을 생성하는 데에는 한계가 있습니다. 예를 들어 비지도 모델이 문서 집합을 5개의 토픽 클러스터로 잘 분류할 수는 있습니다. 하지만 클러스터링 지식만으로는 실시간 언어 번역 앱처럼 별도의 고정밀 시스템을 구현하기에는 충분하지 않습니다.\u003C/p\u003E\r\n\u003Cp\u003E자기 지도 학습은 가공 전 데이터로부터 구조화된 태스크를 생성함으로써, 모델이 이후 실제 다운스트림 태스크에 적용 가능한 특징과 표현을 학습하도록 합니다. 예를 들어 마스킹된 단어를 예측하도록 학습된 모델은 언어 패턴을 학습하며, 이러한 지식은 텍스트 분류나 질의응답과 같은 작업으로 전이될 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E자기 지도 학습 vs. 준지도 학습\u003C/h3\u003E\r\n\u003Cp\u003E준지도 학습은 소량의 레이블 데이터와 대규모 레이블이 없는 데이터를 결합하는 방식입니다. 레이블이 있는 데이터는 모델의 학습을 안정적으로 고정하고 레이블이 없는 데이터는 추가적인 컨텍스트를 제공합니다. 예를 들어 콘텐츠 모더레이션 AI는 수작업으로 레이블이 지정된 소수의 부적절한 이미지나 댓글과 수백만 개의 레이블이 없는 게시물을 함께 사용하여 모델이 대규모 환경에서 유사한 콘텐츠를 식별하도록 학습시킬 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E자기 지도 학습은 작은 규모의 레이블 데이터 세트에도 의존하지 않습니다. 데이터 자체로부터 레이블을 자동으로 생성하므로 의료 영상이나 음성 인식처럼 레이블 데이터가 제한적이거나 비용이 많이 드는 분야에서 특히 유용합니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"and",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-98c436540a","additionalClasses":"headline-decoration","type":"heading2","lines":["자기 지도 학습이 필요한 이유"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-0d1a64b614","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003E자기 지도 학습은 AI 개발에서 가장 큰 과제 중 하나인 대규모 레이블 데이터 세트(지도 학습)에 대한 의존 문제를 해결합니다. 레이블 데이터 세트에 의존할 때 발생하는 주요 문제는 다음과 같습니다.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E비용 및 시간\u003C/b\u003E: 대규모 데이터 세트에 레이블을 수작업으로 지정하는 과정은 비용이 많이 들고 시간이 오래 걸립니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E레이블 데이터 부족\u003C/b\u003E: 법률 문서나 엔터프라이즈 데이터와 같은 전문 분야에서는 레이블 데이터가 부족해 모델을 효과적으로 학습시키기 어렵습니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003ESSL은 이미 존재하는 방대한 양의 레이블이 없는 가공 전 데이터를 활용하여 자체적인 지도 신호를 생성하고 큰 규모의 수작업 없이 유용한 표현을 학습함으로써 이러한 한계를 극복합니다. 이로 인해 다음과 같은 도메인에서 대규모 모델을 보다 효율적으로 훈련할 수 있게 됩니다.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E자연어 처리\u003C/b\u003E: SSL은 어노테이션 수작업 없이 광범위한 텍스트 데이터를 활용해 대규모 언어 모델을 학습할 수 있도록 합니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E컴퓨터 비전 및 음성 인식\u003C/b\u003E: SSL은 이미지 레이블링이나 오디오 전사에 필요한 사람의 노력을 줄이고 모델 정확도를 높입니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_259718203":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"how",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-10b362f437","additionalClasses":"headline-decoration","type":"heading2","lines":["자기 지도 학습의 이점"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-7021587de8","text":"\u003Cp\u003E자기 지도 학습은 최신 AI 시스템에 적합한 여러 장점을 제공합니다. SSL의 이점은 다음과 같습니다.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E레이블 데이터에 대한 의존도 감소\u003C/h3\u003E\n\u003Cp\u003E자기 지도 학습은 가공 전 데이터로부터 학습 신호를 직접 생성하여 수작업 데이터 레이블링의 필요성을 제거합니다. 이러한 기능을 통해 조직은 더 다양한 데이터 세트를 활용하고 여러 소스의 데이터를 결합할 수 있어 분석을 위한 AI의 컨텍스트를 확장하고 가치 창출을 가속화할 수 있습니다. 또한 레이블 데이터가 부족한 전문 의료 영상 분석과 같은 복잡한 분야에서도 AI의 활용이 가능해집니다.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E비용 효율적인 데이터 활용\u003C/h3\u003E\n\u003Cp\u003E자기 지도 학습 모델은 입력 데이터에서 직접 학습 신호를 생성해, 사람이 수행하는 고비용의 어노테이션 작업에 대한 필요성을 최소화합니다. 기존의 레이블이 없는 데이터 구조를 활용함으로써 추가적인 레이블링 비용 없이 데이터 자산의 가치를 높일 수 있습니다. 이로 인해 자기 지도 학습은 레이블이 없는 데이터가 풍부한 데이터 집약적인 분야에서 특히 가치가 높습니다.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E일반화 성능 향상 및 전이 학습 강화\u003C/h3\u003E\n\u003Cp\u003E자기 지도 학습 모델은 데이터의 근본적인 패턴을 학습하며, 이러한 패턴은 새로운 작업으로도 효과적으로 전이됩니다. 파인튜닝을 통해 동일 모델을 여러 다운스트림 애플리케이션에 적용할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E대규모 데이터 세트에 대한 확장성\u003C/h3\u003E\n\u003Cp\u003E오늘날의 방대한 데이터 세트에서 수동 레이블링은 현실적으로 불가능한 작업입니다. 자기 지도 학습은 AI 시스템이 가공 전 데이터에서 직접 학습하도록 하여, 데이터 볼륨이 증가함에 따라 모델도 함께 확장될 수 있도록 합니다.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E모델 성능 강화\u003C/h3\u003E\n\u003Cp\u003ESSL 모델은 데이터의 전체 컨텍스트에서 학습하기 때문에, 지도 학습만으로 학습한 모델보다 다운스트림 태스크에서 더 강력한 결과를 내는 경우가 많습니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_289473020":{"columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"equation",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-20da2f4515","additionalClasses":"headline-decoration","type":"heading2","lines":["자기 지도 학습의 과제"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-30b3575c04","text":"\u003Cp\u003E자기 지도 학습은 분명한 이점을 제공하지만, 구현 과정에서 조직이 해결해야 할 과제도 함께 존재합니다. 주요 과제는 다음과 같습니다.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E계산 복잡성\u003C/h3\u003E\n\u003Cp\u003E자기 지도 학습 모델은 대규모 레이블이 없는 데이터를 장시간 처리해야 하는 경우가 많습니다. 이에 따라 상당한 하드웨어와 클라우드 리소스가 필요하며 더 작은 지도 학습 모델을 학습시키는 것보다 계산 비용이 높아질 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E효과적인 프리텍스트 태스크 설계\u003C/h3\u003E\n\u003Cp\u003E자기 지도 학습은 잘 설계된 프리텍스트 태스크에 의존합니다. 태스크가 너무 단순하면 모델이 유용하지 않은 피처를 학습할 수 있습니다. 태스크 설계가 부적절하면 학습된 표현이 효과적으로 전이되지 않을 수 있습니다. 효과적인 태스크를 설계하려면 도메인 지식과 반복적인 테스트가 필요하며, 이는 자기 지도 학습을 본격적으로 적용하기 전에 선행되어야 합니다.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E모델 성능 평가\u003C/h3\u003E\n\u003Cp\u003E지도 학습에서는 정확도나 정밀도 같은 지표가 학습 중 직접적인 피드백을 제공합니다. 자기 지도 학습은 이런 즉각적인 측정치를 제공하지 않습니다. 모델 품질은 학습된 표현을 다운스트림 태스크에 적용한 이후에야 드러나는 경우가 많아, 피드백이 지연되고 최적화가 더 어려워집니다.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E허위 상관관계 리스크\u003C/h3\u003E\n\u003Cp\u003E자기 지도 학습은 가공 전 데이터에서 생성된 의사 레이블에 의존하므로, 신호에 때때로 노이즈가 많거나 불완전할 수 있습니다. 인간의 감독이 없으면 모델이 학습 데이터에 존재하는 바람직하지 않은 편향성이나 상관관계를 학습할 수 있으며, 이는 이후 다운스트림 애플리케이션에 영향을 줄 수 있습니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"container_copy_copy__1985496925":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"why",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-d4983a65c8","additionalClasses":"headline-decoration","type":"heading2","lines":["자기 지도 학습의 활용 분야 및 예시"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-18bdd6366b","text":"\u003Cp\u003E자기 지도 학습은 레이블이 없는 데이터에서 가치를 도출할 수 있도록 하여, 다양한 산업 전반에 걸친 폭넓은 활용을 지원합니다. SSL의 활용 사례는 다음과 같습니다.\u003C/p\u003E\r\n\u003Ch3\u003E \u003C/h3\u003E\r\n\u003Ch3\u003E자연어 처리\u003C/h3\u003E\r\n\u003Cp\u003E자기 지도 학습은 방대한 텍스트 데이터 세트로 학습되는 BERT 및 GPT 같은 대규모 언어 모델(LLM)을 구동하여 텍스트 분류, 질의 응답, 번역 및 콘텐츠 생성 같은 태스크를 지원합니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E컴퓨터 비전\u003C/h3\u003E\r\n\u003Cp\u003E자기 지도 학습은 수동 어노테이션 없이도 대규모 이미지 및 비디오 컬렉션에서 모델이 학습하도록 지원하며 해당 활용 분야에는 오브젝트 감지, 이미지 분할 및 의료 영상이 포함됩니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E음성 인식 및 오디오 처리\u003C/h3\u003E\r\n\u003Cp\u003E자기 지도 학습은 녹음의 누락되거나 마스킹된 부분을 예측하도록 모델을 학습시켜, 시스템이 가공 전 음성 데이터에서 패턴을 식별하고 학습하도록 지원합니다. 이를 통해 더 정확한 음성 전사, 향상된 음성 비서, 그리고 언어 식별 성능 개선이 가능합니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E사기 감지 및 이상 감지\u003C/h3\u003E\r\n\u003Cp\u003E금융 분야에서 자기 지도 학습은 트랜잭션 데이터의 패턴을 분석해 미세한 불규칙성이나 편차를 식별합니다. 이러한 표현은 시스템에서 잠재적 사기를 플래그 처리하고, 새롭게 등장하는 사기 패턴에 맞춰 적응하도록 돕습니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E추천 시스템\u003C/h3\u003E\r\n\u003Cp\u003E자기 지도 학습은 클릭이나 조회와 같은 암묵적 신호를 제한적인 레이블 데이터와 함께 활용할 수 있습니다. 이를 통해 제품 추천, 광고, 콘텐츠 추천을 맞춤화하여 개인화를 향상시킵니다.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003E자율 주행 및 로봇공학\u003C/h3\u003E\r\n\u003Cp\u003E자기 지도 학습은 차량과 로봇이 센서 및 비디오에서 생성되는 연속적인 가공 전 데이터 스트림으로부터 학습할 수 있도록 합니다. 이러한 학습은 깊이 추정, 내비게이션, 오브젝트 예측 능력을 강화하며, 실제 환경에서의 안전한 운영을 지원합니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container":{"columnClassNames":{},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"advantages",":type":"snowflake-site/components/container",":items":{},":itemsOrder":[],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"container_copy_copy__1190438074":{"columnClassNames":{"container_copy_2083166428":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","container_copy_20831":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"customer",":type":"snowflake-site/components/container",":items":{"container_copy_2083166428":{"columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 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않습니다.\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/ko/legal/\"\u003E법률\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E© 2026 Snowflake Inc. 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