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Data is everywhere and one of the most difficult challenges when it comes to machine learning is how to organize that data in a central location where teams can collaborate and work together.
In this session, we will discuss how a feature store, with its multiple data sources and diversity of connectors, streaming data, and more; allowing teams to work together, share information, and complement each other's work.
An elaborated machine learning pipeline doesn't have to be complex, but rather a transparent structure and contributes to the scalability of models in production for any size organization.
Join us to learn how a feature store, with its multiple data sources and diversity of connectors, streaming data, and online data sets, allows teams to work together, share information, and complement each other's work.
Data is everywhere and one of the most difficult challenges when it comes to machine learning is how to organize that data in a central location where teams can collaborate and work together.
In this session, we will discuss how a feature store, with its multiple data sources and diversity of connectors, streaming data, and more; allowing teams to work together, share information, and complement each other's work.
An elaborated machine learning pipeline doesn't have to be complex, but rather a transparent structure and contributes to the scalability of models in production for any size organization.
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