This is a good idea - every ML operation should have something like this, to store, organize, version data, check for drift, do time-travel, backups/replication et cetera.<p>But to borrow from Steve Jobs, I think this is a feature, not a product. If you've already done the hard work of setting up a data lake or data warehouse in a cloud provider, the cloud provider can give you backups and replication, and even some time-travel. Using something like Delta Lake or even just the standard Kimball DW audit columns will get point-in-time queries. Feature versioning is just query versioning in source control, and if you have schema, you can schema version with views if you need to. If you don't have a data lake, data warehouse ... well, you'll still need to gather and clean all your data before you put it into a feature store, and that's where 90% of the work is.<p>I'd love to learn more, I'm sure I'm missing something, but it seems that they're re-solving the solved part - data storage and versioning. Checking for drift and data integrity is a nice bonus, but again, lots of libraries for that. I guess I could see it being beneficial for ML shops that don't have modern development practices, but if you don't have that, you have bigger problems anyways.