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코드를 더욱더 안전하게 배포 및 처리하기 위해 사용할 수 있는 \u003Ca href=\"https://www.snowflake.com/en/data-cloud/snowpark/\" target=\"_blank\" rel=\"noreferrer noopener\"\u003ESnowpark\u003C/a\u003E 라이브러리 및 런타임의 향상과 확장이었습니다. 당사는 개발자가 Snowpark Python UDF와 저장 프로시저로 데이터를 더 쉽게 처리할 수 있도록 Python \u003Ca href=\"https://lnkd.in/dHMi5WVD\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"\u003E3.9, 3.10\u003C/a\u003E과 비정형 데이터도 지원하기 시작했습니다(현재 공개 미리 보기 제공 중). 또한 우리는 Snowflake에서 코드의 보안과 거버넌스를 강화하기 위해 Python 패키지에 대한 세분화된 허용 목록 및 차단 목록의 공개 미리 보기도 제공하기 시작했습니다. Snowflake 사용자는 이제 외부 네트워크 액세스를 통해 API와 엔드포인트를 안전하게 통합할 수 있습니다(현재 비공개 미리 보기 제공 중). 그뿐만 아니라 이때 사용자 지정한 네트워크 위치에 대한 네트워크 트래픽만을 허용하는 보안 기능을 사용할 수도 있습니다.\u003C/p\u003E\u003Cp\u003E우리는 Snowflake 전반에 걸친 \u003Ca href=\"https://www.snowflake.com/blog//snowpark-ml-api-python-develops-more/\" target=\"_blank\" rel=\"noreferrer noopener\"\u003ESnowpark 혁신\u003C/a\u003E을 통해 데이터 클라우드 내 프로그래밍 기능의 범위와 가능성을 계속 확장해 왔습니다. 위의 혁신은 그 일부에 불과합니다. 당사 고객은 이러한 Snowflake만의 혁신에 따라 다음을 수행할 수 있게 되었습니다.\u003C/p\u003E\u003Cul\u003E\u003C!-- wp:list-item --\u003E\n\u003Cli\u003E엔드 투 엔드 AI/ML 워크플로우 간소화, 가속화 및 스케일링\u003C/li\u003E\n\u003C!-- /wp:list-item --\u003E\n\n\u003C!-- wp:list-item --\u003E\n\u003Cli\u003E스트리밍 기능 확장\u003C/li\u003E\n\u003C!-- /wp:list-item --\u003E\n\n\u003C!-- wp:list-item --\u003E\n\u003Cli\u003E가시성 및 DevOps 경험 향상\u003C/li\u003E\n\u003C!-- /wp:list-item --\u003E\u003C/ul\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"title_v2_1":{"id":"blog-title-719dc8f59a","propertiesId":"aiml","type":"heading4","lines":["엔드 투 엔드 AI/ML 워크플로우 간소화, 가속화 및 스케일링"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"image_1":{"id":"image-e1617172b1","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--4bdba90f-9d61-4b1a-a3c5-30a7d4e13605/screenshot-2023-06-27-at-7.53.44-pm.png?quality=85&preferwebp=true","lazyEnabled":true,"alt":"","height":"952","width":"1856",":type":"snowflake-site/components/image"},"blog_text_2":{"id":"blog-text-c309379fa7","text":"\u003Cp\u003EAI/ML 워크플로우는 크게 개발, 운영, 소비와 같은 모델에 대한 세 단계로 나눌 수 있습니다. 엔드 투 엔드 프로세스에서 AI 기반 통찰력의 가치를 활용하려면 수많은 데이터, 엔지니어링 및 비즈니스 팀이 협업해야 합니다. 그러나 이러한 팀 대부분은 서로 다른 기술을 사용하며, 서로 다른 프로그래밍 언어로 작업합니다. 이에 따라 대부분의 조직에서는 데이터 복사본이 사일로화된 환경으로 이동하고 있으며, 전체 워크플로우를 체계화하고 스케일링하는 데 어려움을 겪고 있습니다. \u003C/p\u003E\u003Cp\u003E당사는 개발을 훨씬 더 폭넓게 지원하기 위해 \u003Ca href=\"https://www.snowflake.com/snowpark-container-services\" target=\"_blank\" rel=\"noreferrer noopener\"\u003ESnowpark 컨테이너 서비스\u003C/a\u003E(비공개 미리 보기 제공 중)를 출시했습니다. 이 서비스를 사용하면 개발자가 GPU와 같은 구성 가능(configurable)한 하드웨어 옵션이 있는 안전한 Snowflake 관리 인프라를 통해 컨테이너화된 모델을 손쉽게 배포, 관리 및 스케일링할 수 있습니다. 이 새로운 Snowpark 런타임의 사용자는 컨테이너에 대한 컴퓨팅 및 클러스터를 관리, 유지하는 복잡한 작업을 직접 처리할 필요도, 관리형 데이터를 자신의 Snowflake 계정 밖으로 이동해 보안 위험에 노출시킬 필요도 없습니다. 이 밖에도 프로그래밍 언어(예: R)와 하드웨어(예: GPU)가 더욱 유연해졌습니다. 이는 개발 속도를 높이고, Snowflake 네이티브 앱으로 호스팅되는 노트북, LLM과 같은 정교한 앱을 배포하는 기능을 개선하는 데 도움이 됩니다. 다음은 AI/ML 개발, 운영과 소비를 간소화하기 위한 기타 Snowpark 혁신입니다. \u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"title_v2_2":{"id":"blog-title-f411308678","propertiesId":"aiml-snowpark-ml-api","type":"heading4","lines":["기능 엔지니어링을 가속화하고 AI/ML 훈련을 간소화하는 Snowpark ML 모델링 API"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_3":{"id":"blog-text-9c899ce30e","text":"\u003Cp\u003EML 모델링 API(공개 미리 보기 제공 중) 및 ML 운영 API(비공개 미리 보기 제공 중)로 이루어진 Snowpark ML API를 사용하면 Snowflake에서의 엔드 투 엔드 ML 개발과 배포가 더욱 쉬워집니다. 개발 측면에서는 \u003Cstrong\u003ESnowpark ML 모델링 API\u003C/strong\u003E가 Snowflake에서 기능 엔지니어링을 스케일 아웃하고 모델 훈련을 간소화합니다.\u003C/p\u003E\u003Cp\u003ESnowpark ML 모델링 API를 이용하면 저장 프로시저를 생성하거나 병렬화를 사용할 필요 없이 Snowflake의 데이터에 대해 기본적으로 Sklearn 스타일의 처리를 구현할 수 있습니다.\u003C/p\u003E\u003Cp\u003E또한 데이터 과학자는 이 API를 이용하면 더 간편한 사용자 경험을 위해 저장 프로시저를 통해 가져오는 대신 데이터에 기본적으로 Sklearn과 XGBoost를 사용하여 모델을 익숙한 API로 Snowpark의 데이터에 대해 직접 훈련시킬 수 있습니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"title_v2_3":{"id":"blog-title-12752d1fb4","propertiesId":"aiml-snowpark","type":"heading4","lines":["조직의 모든 AI/ML 모델을 저장 및 관리하는 Snowpark 모델 레지스트리"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_4":{"id":"blog-text-7d55ab209b","text":"\u003Cp\u003E데이터 과학자는 모델이 개발된 다음에는 Snowpark 모델 레지스트리(비공개 미리 보기 제공 중)가 포함된 Snowpark ML 운영 API를 통해 Snowflake에서 모델을 원활하게 배포할 수 있습니다. 이는 조직의 ML 모델에 대한 통합 리포지토리를 제공하여 머신 러닝 모델 운영(MLOps)을 간소화하고 스케일링합니다. \u003C/p\u003E\u003Cp\u003E해당 레지스트리는 모델의 중앙 집중식 게시와 검색을 제공합니다. 이에 따라 데이터 과학자가 ML 엔지니어에게 성공적인 실험을 전달하여 Snowflake 인프라에서 프로덕션 모델로 배포하는 프로세스의 일환인 협업이 간소화됩니다. \u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"title_v2_4":{"id":"blog-title-7722fd182d","propertiesId":"snowflake-streamlit","type":"heading4","lines":["데이터와 모델을 대화형 앱으로 구현하는 Snowflake의 Streamlit"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_5":{"id":"blog-text-70fbd26bb3","text":"\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/data-cloud/streamlit-in-snowflake\" target=\"_blank\" rel=\"noreferrer noopener\"\u003ESnowflake의 Streamlit\u003C/a\u003E(곧 공개 미리 보기 제공 예정)은 Python으로 구축된 대화형 앱을 통해 데이터와 ML 모델에 생명을 불어넣습니다. 이는 구성 요소가 풍부하고 사용자 친화적인 앱 개발용 Streamlit 오픈소스 라이브러리를 Snowflake 플랫폼의 확장성(scalability), 안정성, 보안 및 거버넌스와 결합합니다. \u003C/p\u003E\u003Cp\u003EStreamlit은 데이터 과학자와 Python 개발자가 데이터와 모델을 대기업에까지 적합한(enterprise-ready) 대화형 애플리케이션으로 빠르게 전환할 수 있는 기능을 제공합니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"title_v2_5":{"id":"blog-title-d174dd5f50","propertiesId":"snowflake","type":"heading4","lines":["Snowflake의 간소화된 스트리밍 파이프라인"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_6":{"id":"blog-text-917365ae7e","text":"\u003Cp\u003E당사는 동적 테이블(공개 미리 보기 제공 중)과 함께 스트리밍 기능을 확장하고 있습니다. 동적 테이블은 배치 및 스트리밍 데이터를 모두 변환하기 위한 연속 데이터 파이프라인을 크게 간소화합니다. 이제 사용자는 스트리밍 데이터 파이프라인을 CTAS(Create Table as Select)문만큼 쉽게 손에 넣을 수 있습니다. Snowflake는 \u003Ca href=\"https://www.snowflake.com/blog/snowpipe-streaming-public-preview/\" target=\"_blank\" rel=\"noreferrer noopener\"\u003ESnowpipe 스트리밍\u003C/a\u003E(곧 공식 출시(GA) 예정)으로 배치 시스템과 스트리밍 시스템 간의 경계를 허물고, 스트리밍 파이프라인을 그 어느 때보다도 더 쉽게 만들고 있습니다. \u003C/p\u003E\u003Cp\u003E동적 테이블은 쿼리로 정의된 테이블이자, 쿼리 결과를 테이블에 계속 유지시키는 새로운 테이블입니다. 동적 테이블은 여러 소스 객체를 조인(join)하고 집계할 수 있으며, 소스 변경에 따라 결과를 점차 업데이트할 수 있습니다. 고객은 동적 테이블을 통해 업데이트 빈도에 대한 정보 및 쿼리를 제공하고, Snowflake는 자동으로 결과를 구체화합니다. 이는 즉 데이터 사전 컴퓨팅이 데이터 엔지니어가 수동으로 완료해야 하는 과업으로 남는 대신 자동화된다는 뜻입니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"title_v2_6":{"id":"blog-title-3d41961dd6","propertiesId":"","type":"heading4","lines":["가시성 및 엔드 투 엔드 개발자 경험 개선"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_7":{"id":"blog-text-cf4e977c32","text":"\u003Cp\u003E당사는 개발자가 엔드 투 엔드 애플리케이션, 파이프라인과 ML 모델을 더 쉽고, 빠르고, 간편하게 구축하기를 바랍니다. 따라서 우리는 개발자에게 익숙한 기능과 경험을 제공해 개발자가 더 효율적으로 작업하는 데 도움이 되는 것을 목표로 하고 있습니다. 우리는 이를 위해 Summit에서 DevOps 및 가시성(observability) 기능을 출시했습니다. 이러한 기능은 개발자가 협업을 통해 구축하고, 쉽게 테스트하고, 문제를 더 빠르게 해결하고, 안정적으로 운영하고, 전반적으로 생산성을 높일 수 있도록 지원합니다. \u003C/p\u003E\u003Cp\u003E또한 여기에는 Git 통합(비공개 미리 보기 제공 중)과 같은 기능이 포함되어 있어 애플리케이션 코드가 git 및 git 워크플로우와 쉽게 결합될 수 있습니다. 사용자는 Snowflake 바로 안에 있는 Git 리포지토리의 자산을 보고, 실행하고, 편집하고, 이에 대해 협업할 수 있습니다. \u003C/p\u003E\u003Cp\u003E당사는 Snowflake CLI의 비공개 미리 보기도 발표했습니다. Snowflake CLI는 명시적으로 Snowflake의 앱 중심 워크로드용으로 설계된 오픈소스 명령줄 인터페이스입니다. 개발자는 간단한 명령을 통해 Streamlit, 네이티브 앱, Snowpark 컨테이너, Snowpark와 같은 워크로드에서 Snowflake에서 실행되는 앱을 생성, 관리, 업데이트 및 확인할 수 있습니다.\u003C/p\u003E\u003Cp\u003E이벤트 테이블을 사용하는 로깅 및 추적(공개 미리 보기 제공 중)은 문제 해결을 위해 Snowflake 내부와 외부에서 Snowflake 엔진으로 로그를 탐색하며, Snowflake의 코드에 대해 더 나은 디버깅 기능을 제공합니다. \u003C/p\u003E\u003Cp\u003E이 모든 기능은 Snowflake에서 앱과 데이터 모두의 전체 DevOps 및 소프트웨어 개발 수명 주기에 대해 깔끔하게 결합되어 더욱 생산적인 개발자 워크플로우를 구현해 줍니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"title_v2_7":{"id":"blog-title-73f05750b9","propertiesId":"","type":"heading4","lines":["다음 단계:"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_8":{"id":"blog-text-1ee1f2cda7","text":"\u003Cp\u003ESnowflake는 고객이 엔터프라이즈 데이터에 대해 LLM을 안전하게 실행하도록 지원하고 AI 기반 기능을 내장 기능 및 UI의 형태로 제공하는 등, 데이터에 생성형 AI를 도입하고 있습니다. 이 모든 것이 어떻게 작동하는지 살펴보려면 Summit에서 ML 데모를 확인하세요. \u003Cbr\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"title_v2_8":{"id":"blog-title-d1852268a1","propertiesId":"","type":"heading5","lines":["더 알아보기:"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_9":{"id":"blog-text-b6de071f94","text":"\u003Cul\u003E\u003C!-- wp:list-item --\u003E\n\u003Cli\u003E이 \u003Ca href=\"https://quickstarts.snowflake.com/guide/getting_started_with_dataengineering_ml_using_snowpark_python/index.html#0\" target=\"_blank\" rel=\"noreferrer noopener\"\u003E빠른 시작 가이드\u003C/a\u003E에 따라 Python용 Snowpark를 통해 데이터 엔지니어링과 ML을 시작하세요.\u003C/li\u003E\n\u003C!-- /wp:list-item --\u003E\n\n\u003C!-- wp:list-item --\u003E\n\u003Cli\u003E이 \u003Ca href=\"https://quickstarts.snowflake.com/guide/getting_started_with_snowpark_in_snowflake_python_worksheets/#0\" target=\"_blank\" rel=\"noreferrer noopener\"\u003E빠른 시작 가이드\u003C/a\u003E를 사용하여 Snowflake Python 워크시트(공개 미리 보기 제공 중)에서 Snowpark를 체험해 보세요. \u003C/li\u003E\n\u003C!-- /wp:list-item --\u003E\n\n\u003C!-- wp:list-item --\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.linkedin.com/company/3653845/\" target=\"_blank\" rel=\"noreferrer noopener\"\u003ELinkedIn\u003C/a\u003E 및 \u003Ca href=\"https://twitter.com/SnowflakeDB\" target=\"_blank\" rel=\"noreferrer noopener\"\u003ETwitter\u003C/a\u003E에서 Snowflake의 최신 소식과 발표를 확인해 보세요. \u003C/li\u003E\n\u003C!-- /wp:list-item --\u003E\u003C/ul\u003E\u003Cp\u003E참고: 이 내용은 2023. 6. 28에 게시된 컨텐츠(Snowflake Expands Programmability to Bolster Support for AI/ML and Streaming Pipeline Development)에서 번역되었습니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["blog_text_1","title_v2_1","image_1","blog_text_2","title_v2_2","blog_text_3","title_v2_3","blog_text_4","title_v2_4","blog_text_5","title_v2_5","blog_text_6","title_v2_6","blog_text_7","title_v2_7","blog_text_8","title_v2_8","blog_text_9"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"columnClassNames":{},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,"appliedCssClassNames":"snowflake-responsive-component-top-padding-medium",":items":{},":itemsOrder":[],":type":"wcm/foundation/components/responsivegrid"},"container_author_chip":{"columnClassNames":{"author_chip":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 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