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Bridging the Gap between Data Pipelines and Machine Learning with MLOps feat. Michael Galarnyk of cnvrg.io
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Manage episode 347273719 series 1414202
Making a data pipeline fit for machine learning use cases requires more than just additional data monitoring. Furthermore, bringing machine learning into production has traditionally required a lot of manual setup and configuration, even for toy ML pipelines. These manual methods are not reproducible, don’t autoscale, require significant technical expertise, and are error-prone. Among other things, this episode will go over MLOps, a set of practices aiming to deploy and maintain machine learning models in production reliably and efficiently.
175 epizódok
Archivált sorozatok ("Inaktív feed" status)
When? This feed was archived on April 10, 2023 06:03 (). Last successful fetch was on January 17, 2023 18:55 ()
Why? Inaktív feed status. A szervereink huzamosabb ideig nem tudtak érvényes podcast-feedet megjeleníteni.
What now? You might be able to find a more up-to-date version using the search function. This series will no longer be checked for updates. If you believe this to be in error, please check if the publisher's feed link below is valid and contact support to request the feed be restored or if you have any other concerns about this.
Manage episode 347273719 series 1414202
Making a data pipeline fit for machine learning use cases requires more than just additional data monitoring. Furthermore, bringing machine learning into production has traditionally required a lot of manual setup and configuration, even for toy ML pipelines. These manual methods are not reproducible, don’t autoscale, require significant technical expertise, and are error-prone. Among other things, this episode will go over MLOps, a set of practices aiming to deploy and maintain machine learning models in production reliably and efficiently.
175 epizódok
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