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O'Reilly Radar에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 O'Reilly Radar 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
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Why Companies are in Need of Data Lineage Solutions

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Manage episode 232064028 series 1427720
O'Reilly Radar에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 O'Reilly Radar 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
In this episode of the Data Show, I spoke with Neelesh Salian, software engineer at Stitch Fix, a company that combines machine learning and human expertise to personalize shopping. As companies integrate machine learning into their products and systems, there are important foundational technologies that come into play. This shouldn’t come as a shock, as current machine learning and AI technologies require large amounts of data—specifically, labeled data for training models. There are also many other considerations—including security, privacy, reliability/safety—that are encouraging companies to invest in a suite of data technologies. In conversations with data engineers, data scientists, and AI researchers, the need for solutions that can help track data lineage and provenance keeps popping up. There are several San Francisco Bay Area companies that have embarked on building data lineage systems—including Salian and his colleagues at Stitch Fix. I wanted to find out how they arrived at the decision to build such a system and what capabilities they are building into it.
  continue reading

443 에피소드

Artwork
icon공유
 
Manage episode 232064028 series 1427720
O'Reilly Radar에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 O'Reilly Radar 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
In this episode of the Data Show, I spoke with Neelesh Salian, software engineer at Stitch Fix, a company that combines machine learning and human expertise to personalize shopping. As companies integrate machine learning into their products and systems, there are important foundational technologies that come into play. This shouldn’t come as a shock, as current machine learning and AI technologies require large amounts of data—specifically, labeled data for training models. There are also many other considerations—including security, privacy, reliability/safety—that are encouraging companies to invest in a suite of data technologies. In conversations with data engineers, data scientists, and AI researchers, the need for solutions that can help track data lineage and provenance keeps popping up. There are several San Francisco Bay Area companies that have embarked on building data lineage systems—including Salian and his colleagues at Stitch Fix. I wanted to find out how they arrived at the decision to build such a system and what capabilities they are building into it.
  continue reading

443 에피소드

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