How to Build Flexible, Portable ML Stacks with Kubeflow and Elastifile (Next Rewind '18)
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Google Cloud Platform1.17 млн
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24.09.18 – 12 9351:01Популярные
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39 дней – 2 8034:48Опубликовано 24 сентября 2018, 19:32
Building any production-ready machine learning system involves various components, often mixing vendors, and hand-rolled solutions. Connecting and managing these services for even moderately sophisticated setups introduces huge barriers of complexity, with data management often emerging as an especially daunting concern. In this session, we demonstrate how Kubeflow’s support portable ML pipelines integrates with Elastifile’s scalable, high-performance file services to address these challenges both on-premises and in Google Cloud. Join us and learn how to make ML on Kubernetes easy, fast, and extensible.
Original talk by David Aronchick and Allon Cohen
Rewind by Cassie Kozyrkov
Watch the full session here → bit.ly/2pwkB27
Watch other recaps here → bit.ly/NextRewind2018
Watch more Machine Learning & AI sessions here → bit.ly/2zGKfcg
Next ‘18 All Sessions playlist → bit.ly/Allsessions
Subscribe to the Google Cloud Platform channel! → bit.ly/GCloudPlatform
Original talk by David Aronchick and Allon Cohen
Rewind by Cassie Kozyrkov
Watch the full session here → bit.ly/2pwkB27
Watch other recaps here → bit.ly/NextRewind2018
Watch more Machine Learning & AI sessions here → bit.ly/2zGKfcg
Next ‘18 All Sessions playlist → bit.ly/Allsessions
Subscribe to the Google Cloud Platform channel! → bit.ly/GCloudPlatform
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