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Dataplatform architecture has an interesting history. A read-optimized platform that can integrate data from multiple applications emerged. In another decade, the internet and mobile started the generate data of unforeseen volume, variety and velocity. It required a different dataplatform solution.
Database metadata can be expressed in various formats, including schema.org and DCAT. Unfortunately, these formats weren’t made with machine learning data in mind. Google has recently introduced Croissant, a new format for metadata in ML-ready datasets. Users can then publish their datasets.
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There’s no component that stores metadata about this feature store? Mikiko Bazeley: In the case of the literal feature store, all it does is store features and metadata. We’re assuming that data scientists, for the most part, don’t want to write transformations elsewhere. Mikiko Bazeley: 100%. We offer that.
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