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Feathr: LinkedIn's High-performance Feature Store // David Stein // Coffee Sessions #120
MLOps.community
Decoupling Machine Learning Features From Named Features
There's a feature that is registered in a registry and has a name. You can rely on that name meaning the same thing in the different parts of the ecosystem. Being able to rely on that instead of named features in a registry does help you think about building and deploying a machine learning model. I love this idea of decoupling the features that you can reason with From the code too and then bringing that standardization so that across the company you understand it will be the same. We at linkedin we have a kind of a culture of open sourcing our infrastructure pieces that we build that we're proud of And that we think May be of interest in use to the outside world.
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