際際滷shows by User: DavidStein1 / http://www.slideshare.net/images/logo.gif 際際滷shows by User: DavidStein1 / Mon, 25 Feb 2019 19:43:35 GMT 際際滷Share feed for 際際滷shows by User: DavidStein1 Frame - Feature Management for Productive Machine Learning /slideshow/frame-feature-management-for-productive-machine-learning/133272705 frame-mlplatformsmeetup-16august2018-190225194335
Presented at the ML Platforms Meetup at Pinterest HQ in San Francisco on August 16, 2018. Abstract: At LinkedIn we observed that much of the complexity in our machine learning applications was in their feature preparation workflows. To address this problem, we built Frame, a shared virtual feature store that provides a unified abstraction layer for accessing features by name. Frame removes the need for feature consumers to deal directly with underlying data sources, which are often different across computing environments. By simplifying feature preparation, Frame has made ML applications at LinkedIn easier to build, modify, and understand.]]>
Presented at the ML Platforms Meetup at Pinterest HQ in San Francisco on August 16, 2018. Abstract: At LinkedIn we observed that much of the complexity in our machine learning applications was in their feature preparation workflows. To address this problem, we built Frame, a shared virtual feature store that provides a unified abstraction layer for accessing features by name. Frame removes the need for feature consumers to deal directly with underlying data sources, which are often different across computing environments. By simplifying feature preparation, Frame has made ML applications at LinkedIn easier to build, modify, and understand.]]> Mon, 25 Feb 2019 19:43:35 GMT /slideshow/frame-feature-management-for-productive-machine-learning/133272705 DavidStein1@slideshare.net(DavidStein1) Frame - Feature Management for Productive Machine Learning DavidStein1 Presented at the ML Platforms Meetup at Pinterest HQ in San Francisco on August 16, 2018. Abstract: At LinkedIn we observed that much of the complexity in our machine learning applications was in their feature preparation workflows. To address this problem, we built Frame, a shared virtual feature store that provides a unified abstraction layer for accessing features by name. Frame removes the need for feature consumers to deal directly with underlying data sources, which are often different across computing environments. By simplifying feature preparation, Frame has made ML applications at LinkedIn easier to build, modify, and understand. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/frame-mlplatformsmeetup-16august2018-190225194335-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Presented at the ML Platforms Meetup at Pinterest HQ in San Francisco on August 16, 2018. Abstract: At LinkedIn we observed that much of the complexity in our machine learning applications was in their feature preparation workflows. To address this problem, we built Frame, a shared virtual feature store that provides a unified abstraction layer for accessing features by name. Frame removes the need for feature consumers to deal directly with underlying data sources, which are often different across computing environments. By simplifying feature preparation, Frame has made ML applications at LinkedIn easier to build, modify, and understand.
Frame - Feature Management for Productive Machine Learning from David Stein
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