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Få den indsigt, der skal til for at træffe oplyste beslutninger og differentiere dig fra konkurrenterne. Se for eksempel, hvordan du kan analysere og anvende store og komplekse datamængder fra et utal af interne og eksterne kilder på en nem og overskuelig måde. Selv fra komplekse tredje-parts data kan du hente værdifuld viden.
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[211] 인공지능이 인공지능 챗봇을 만든다
[211] 인공지능이 인공지능 챗봇을 만든다
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[233] 대형 컨테이너 클러스터에서의 고가용성 Network Load Balancing: Maglev Hashing Scheduler i...
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[215] Druid로 쉽고 빠르게 데이터 분석하기
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[211] 인공지능이 인공지능 챗봇을 만든다
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[233] 대형 컨테이너 클러스터에서의 고가용성 Network Load Balancing: Maglev Hashing Scheduler in IPVS, Linux Kernel
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[215] Druid로 쉽고 빠르게 데이터 분석하기
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[236] 스트림 저장소 최적화 이야기: 아파치 드루이드로부터 얻은 교훈
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[236] 스트림 저장소 최적화 이야기: 아파치 드루이드로부터 얻은 교훈
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[235]Wikipedia-scale Q&A
[235]Wikipedia-scale Q&A
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[244]로봇이 현실 세계에 대해 학습하도록 만들기
[244]로봇이 현실 세계에 대해 학습하도록 만들기
[244]로봇이 현실 세계에 대해 학습하도록 만들기
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[243] Deep Learning to help student’s Deep Learning
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[234]Fast & Accurate Data Annotation Pipeline for AI applications
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그림이 정상 출력되는 다음 링크의 자료를 확인해 주세요. https://www.slideshare.net/deview/233-network-load-balancing-maglev-hashing-scheduler-in-ipvs-linux-kernel
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[216]Search Reliability Engineering (부제: 지진에도 흔들리지 않는 네이버 검색시스템)
[216]Search Reliability Engineering (부제: 지진에도 흔들리지 않는 네이버 검색시스템)
[216]Search Reliability Engineering (부제: 지진에도 흔들리지 않는 네이버 검색시스템)
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[214] Ai Serving Platform: 하루 수 억 건의 인퍼런스를 처리하기 위한 고군분투기
[214] Ai Serving Platform: 하루 수 억 건의 인퍼런스를 처리하기 위한 고군분투기
[214] Ai Serving Platform: 하루 수 억 건의 인퍼런스를 처리하기 위한 고군분투기
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[213] Fashion Visual Search
[213] Fashion Visual Search
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[232] TensorRT를 활용한 딥러닝 Inference 최적화
[232] TensorRT를 활용한 딥러닝 Inference 최적화
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[212]C3, 데이터 처리에서 서빙까지 가능한 하둡 클러스터
[212]C3, 데이터 처리에서 서빙까지 가능한 하둡 클러스터
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[211] 인공지능이 인공지능 챗봇을 만든다
[211] 인공지능이 인공지능 챗봇을 만든다
[233] 대형 컨테이너 클러스터에서의 고가용성 Network Load Balancing: Maglev Hashing Scheduler i...
[233] 대형 컨테이너 클러스터에서의 고가용성 Network Load Balancing: Maglev Hashing Scheduler i...
[215] Druid로 쉽고 빠르게 데이터 분석하기
[215] Druid로 쉽고 빠르게 데이터 분석하기
[245]Papago Internals: 모델분석과 응용기술 개발
[245]Papago Internals: 모델분석과 응용기술 개발
[236] 스트림 저장소 최적화 이야기: 아파치 드루이드로부터 얻은 교훈
[236] 스트림 저장소 최적화 이야기: 아파치 드루이드로부터 얻은 교훈
[235]Wikipedia-scale Q&A
[235]Wikipedia-scale Q&A
[244]로봇이 현실 세계에 대해 학습하도록 만들기
[244]로봇이 현실 세계에 대해 학습하도록 만들기
[243] Deep Learning to help student’s Deep Learning
[243] Deep Learning to help student’s Deep Learning
[234]Fast & Accurate Data Annotation Pipeline for AI applications
[234]Fast & Accurate Data Annotation Pipeline for AI applications
Old version: [233]대형 컨테이너 클러스터에서의 고가용성 Network Load Balancing
Old version: [233]대형 컨테이너 클러스터에서의 고가용성 Network Load Balancing
[226]NAVER 광고 deep click prediction: 모델링부터 서빙까지
[226]NAVER 광고 deep click prediction: 모델링부터 서빙까지
[225]NSML: 머신러닝 플랫폼 서비스하기 & 모델 튜닝 자동화하기
[225]NSML: 머신러닝 플랫폼 서비스하기 & 모델 튜닝 자동화하기
[224]네이버 검색과 개인화
[224]네이버 검색과 개인화
[216]Search Reliability Engineering (부제: 지진에도 흔들리지 않는 네이버 검색시스템)
[216]Search Reliability Engineering (부제: 지진에도 흔들리지 않는 네이버 검색시스템)
[214] Ai Serving Platform: 하루 수 억 건의 인퍼런스를 처리하기 위한 고군분투기
[214] Ai Serving Platform: 하루 수 억 건의 인퍼런스를 처리하기 위한 고군분투기
[213] Fashion Visual Search
[213] Fashion Visual Search
[232] TensorRT를 활용한 딥러닝 Inference 최적화
[232] TensorRT를 활용한 딥러닝 Inference 최적화
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Deview2012 키노트 #2
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