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Featureform and the Future of MLOps with Simba Khadder

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Manage episode 344615846 series 2626943
Content provided by Eric Anderson. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Eric Anderson or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

Eric Anderson (@ericmander) and Simba Khadder (@simba_khadder) explore Featureform, the “virtual” feature store platform that aims to standardize data pipelines for machine learning. Contributor is no stranger to feature stores, but Simba has a broader definition than most. Join us to learn how Featureform enables data scientists and machine learning practitioners to solve a common, but rarely addressed organizational problem.

Subscribe to Contributor on Substack for email notifications, and join our Slack community!

In this episode we discuss:

  • How there is no standard or north star for MLOps
  • Why enterprise is where Featureform’s value shines
  • MLPlatform problems vs MLOps problems
  • Why copy/paste and Git don’t cut it
  • Deploying MLOps solutions that make data scientists and everyone else happy

Links:

Other episodes:

  continue reading

85 episodes

Artwork
iconShare
 
Manage episode 344615846 series 2626943
Content provided by Eric Anderson. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Eric Anderson or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

Eric Anderson (@ericmander) and Simba Khadder (@simba_khadder) explore Featureform, the “virtual” feature store platform that aims to standardize data pipelines for machine learning. Contributor is no stranger to feature stores, but Simba has a broader definition than most. Join us to learn how Featureform enables data scientists and machine learning practitioners to solve a common, but rarely addressed organizational problem.

Subscribe to Contributor on Substack for email notifications, and join our Slack community!

In this episode we discuss:

  • How there is no standard or north star for MLOps
  • Why enterprise is where Featureform’s value shines
  • MLPlatform problems vs MLOps problems
  • Why copy/paste and Git don’t cut it
  • Deploying MLOps solutions that make data scientists and everyone else happy

Links:

Other episodes:

  continue reading

85 episodes

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