데이터 과학자를 위한 Data Science Workspace 시작하기

Adobe Experience Platform의 데이터 과학 Workspace에 대해 알아봅니다. 이 재생 목록은 JupyterLab Notebooks를 사용하여 통찰력을 얻고 데이터를 쿼리하는 방법, 프로필이 활성화된 데이터 세트를 만드는 방법, 자동화된 머신 러닝 모델을 게시하는 방법 및 Adobe과 Adobe 이외의 애플리케이션에 머신 러닝을 통한 통찰력을 활성화하는 방법에 대해 알아보고자 하는 데이터 과학자를 위해 설계되었습니다.

https://video.tv.adobe.com/v/3412914?learn=on

데이터 과학 작업 영역 개요

데이터 과학 작업 영역 개요

Adobe Experience Platform에서 머신 러닝의 비전은 Adobe 제품, 고객 및 파트너의 도메인 전문 지식을 사용하여 데이터 과학을 대중화하고 차세대 고객 경험을 강화할 수 있는 지능형 서비스 생태계를 만드는 것입니다. Data Science Workspace을 사용하면 옴니채널 데이터에 쉽게 액세스하고 모델을 구축하며 원클릭 배포로 모델을 운영하고 실시간 고객 프로필을 통해 공유하여 모델 통찰력을 사용할 수 있습니다. 이 비디오에서는 데이터 과학 Workspace이 무엇이며 비즈니스에 제공하는 가치에 대한 개요를 제공합니다.

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https://video.tv.adobe.com/v/332368?learn=on

데이터 과학 Workspace 아키텍처 개요

데이터 과학 Workspace 아키텍처 개요

이 비디오에서는 주요 아키텍처에 대해 설명하고 Adobe Experience Platform에서 데이터 과학 Workspace의 기본 구성 요소를 보여 줍니다.

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https://video.tv.adobe.com/v/333312?learn=on

강의 스키마 및 데이터 세트 만들기

강의 스키마 및 데이터 세트 만들기

나머지 과정에서 사용되는 Data Science Workspace 과정 데이터 세트 및 스키마를 만드는 방법을 알아봅니다.

246

https://video.tv.adobe.com/vc/333312/kor.json

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https://video.tv.adobe.com/v/345260?learn=on

JupyterLab 노트북에 데이터 로드

JupyterLab 노트북에 데이터 로드

이 비디오는 JupyterLab 노트북을 만들고 Adobe Experience Platform에서 데이터를 로드하는 방법을 보여 줍니다. 또한 많은 양의 데이터를 사용하여 작업할 때 노트북의 성능을 향상시키는 방법도 보여줍니다.

224

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https://video.tv.adobe.com/v/333311?learn=on

데이터 과학 Workspace에서 데이터 쿼리 및 검색

데이터 과학 Workspace에서 데이터 쿼리 및 검색

Adobe Experience Platform에서는 쿼리 서비스를 JupyterLab에 표준 기능으로 통합하여 데이터 과학 Workspace에서 SQL(Structured Query Language)을 사용할 수 있습니다.

555

https://video.tv.adobe.com/vc/333311/kor.json

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https://video.tv.adobe.com/v/333310?learn=on

데이터 과학 Workspace의 탐색적 데이터 분석

데이터 과학 Workspace의 탐색적 데이터 분석

EDA(Exploratory Data Analysis) 튜토리얼은 데이터에서 패턴을 찾고, 데이터 온전성을 확인하고, 예측 모델에 대한 관련 데이터를 요약하는 데 도움이 되도록 설계되었습니다.

654

https://video.tv.adobe.com/vc/333310/kor.json

{ "description": "EDA(Exploratory Data Analysis) 튜토리얼은 데이터에서 패턴을 찾고, 데이터 온전성을 확인하고, 예측 모델에 대한 관련 데이터를 요약하는 데 도움이 되도록 설계되었습니다.", "duration": "PT0H10M54S", "embedUrl": "https://video.tv.adobe.com/v/333310/", "name": "데이터 과학 Workspace의 탐색적 데이터 분석", "thumbnailUrl": [ "https://images-tv.adobe.com/mpcv3/eafa326a-6d93-48ba-a6ff-d9b7d64a56e0/41825c6c-eb27-4002-ab7b-9e4c299861c1/64d3c78ea6264f6db67e8f3a76b25468_1622665337-100x56.jpg", "https://images-tv.adobe.com/mpcv3/eafa326a-6d93-48ba-a6ff-d9b7d64a56e0/41825c6c-eb27-4002-ab7b-9e4c299861c1/64d3c78ea6264f6db67e8f3a76b25468_1622665338-150x84.jpg", "https://images-tv.adobe.com/mpcv3/eafa326a-6d93-48ba-a6ff-d9b7d64a56e0/41825c6c-eb27-4002-ab7b-9e4c299861c1/64d3c78ea6264f6db67e8f3a76b25468_1622665338-200x113.jpg", "https://images-tv.adobe.com/mpcv3/eafa326a-6d93-48ba-a6ff-d9b7d64a56e0/41825c6c-eb27-4002-ab7b-9e4c299861c1/64d3c78ea6264f6db67e8f3a76b25468_1622665338-220x124.jpg", "https://images-tv.adobe.com/mpcv3/eafa326a-6d93-48ba-a6ff-d9b7d64a56e0/41825c6c-eb27-4002-ab7b-9e4c299861c1/64d3c78ea6264f6db67e8f3a76b25468_1622665338-236x133.jpg", "https://images-tv.adobe.com/mpcv3/eafa326a-6d93-48ba-a6ff-d9b7d64a56e0/41825c6c-eb27-4002-ab7b-9e4c299861c1/64d3c78ea6264f6db67e8f3a76b25468_1622665338-290x186.jpg", "https://images-tv.adobe.com/mpcv3/eafa326a-6d93-48ba-a6ff-d9b7d64a56e0/41825c6c-eb27-4002-ab7b-9e4c299861c1/64d3c78ea6264f6db67e8f3a76b25468_1622665338-420x236.jpg", "https://images-tv.adobe.com/mpcv3/eafa326a-6d93-48ba-a6ff-d9b7d64a56e0/41825c6c-eb27-4002-ab7b-9e4c299861c1/64d3c78ea6264f6db67e8f3a76b25468_1622665339-1920x1080.jpg", "https://images-tv.adobe.com/mpcv3/eafa326a-6d93-48ba-a6ff-d9b7d64a56e0/41825c6c-eb27-4002-ab7b-9e4c299861c1/64d3c78ea6264f6db67e8f3a76b25468_1622665339-640x360.jpg", "https://images-tv.adobe.com/mpcv3/eafa326a-6d93-48ba-a6ff-d9b7d64a56e0/41825c6c-eb27-4002-ab7b-9e4c299861c1/64d3c78ea6264f6db67e8f3a76b25468_1622665339-666x374.jpg", "https://images-tv.adobe.com/mpcv3/eafa326a-6d93-48ba-a6ff-d9b7d64a56e0/41825c6c-eb27-4002-ab7b-9e4c299861c1/64d3c78ea6264f6db67e8f3a76b25468_1622665339-720x405.jpg", "https://images-tv.adobe.com/mpcv3/eafa326a-6d93-48ba-a6ff-d9b7d64a56e0/41825c6c-eb27-4002-ab7b-9e4c299861c1/64d3c78ea6264f6db67e8f3a76b25468_1622665339-960x540.jpg" ], "@type": [ "VideoObject", "LearningResource" ], "uploadDate": "2021-05-15T22:05:37Z", "hasPart": [ { "@type": "Clip", "name": "What is EDA?", "startOffset": 34, "endOffset": 140, "url": "https://video.tv.adobe.com/v/333310/?t=34" }, { "@type": "Clip", "name": "Start demo", "startOffset": 141, "endOffset": 206, "url": "https://video.tv.adobe.com/v/333310/?t=141" }, { "@type": "Clip", "name": "Set prediction variable", "startOffset": 207, "endOffset": 234, "url": "https://video.tv.adobe.com/v/333310/?t=207" }, { "@type": "Clip", "name": "Data aggregation and goal creation", "startOffset": 235, "endOffset": 272, "url": "https://video.tv.adobe.com/v/333310/?t=235" }, { "@type": "Clip", "name": "Merge features with a goal", "startOffset": 273, "endOffset": 315, "url": "https://video.tv.adobe.com/v/333310/?t=273" }, { "@type": "Clip", "name": "Missing values and outliers", "startOffset": 316, "endOffset": 423, "url": "https://video.tv.adobe.com/v/333310/?t=316" }, { "@type": "Clip", "name": "Univariate analysis", "startOffset": 424, "endOffset": 560, "url": "https://video.tv.adobe.com/v/333310/?t=424" }, { "@type": "Clip", "name": "Bivariate analysis", "startOffset": 561, "endOffset": 623, "url": "https://video.tv.adobe.com/v/333310/?t=561" }, { "@type": "Clip", "name": "Important numerical features", "startOffset": 624, "endOffset": 654, "url": "https://video.tv.adobe.com/v/333310/?t=624" } ], "educationLevel": [ "Beginner" ], "learningResourceType": "데이터 과학 Workspace의 탐색적 데이터 분석" }

https://video.tv.adobe.com/v/333380?learn=on

레서피, 모델 및 서비스 개요

레서피, 모델 및 서비스 개요

Adobe Experience Platform Data Science Workspace의 레시피, 모델 및 서비스에 대해 알아봅니다.

528

https://video.tv.adobe.com/vc/333380/kor.json

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모델 성능 분석

모델 성능 분석

혼동 행렬, 정확도, 리콜 및 정밀도와 같이 모델의 성능을 분석하는 데 사용되는 다양한 방법에 대해 알아봅니다.

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레시피 빌더 템플릿을 사용하여 모델 구축

레시피 빌더 템플릿을 사용하여 모델 구축

이 비디오는 JupyterLab 런처의 레시피 빌더 템플릿을 사용하여 성향 모델을 교육하고 점수화하고 레시피를 만드는 방법을 보여 줍니다.

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교육된 모델 만들기 및 게시

교육된 모델 만들기 및 게시

JupyterLab 레시피 빌더 전자 필기장으로 만든 레시피를 사용하여 모델을 만들고, 교육하고, 평가하고, 게시하는 방법에 대해 알아봅니다.

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서비스에 대한 자동화된 교육 및 채점 예약

서비스에 대한 자동화된 교육 및 채점 예약

Data Science Workspace에서 서비스에 대한 자동화된 교육 및 점수를 설정하는 방법에 대해 알아봅니다.

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세분화에서 머신 러닝 출력 사용

세분화에서 머신 러닝 출력 사용

Data Science Workspace 모델 출력을 실시간 고객 프로필 및 세분화에서 사용하는 방법에 대해 알아봅니다.

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