How to Efficiently Manage Multiple Datasets in Basic.ai Without Slowing Down the Workflow?

Hey everyone,

I’ve recently started using Basic.ai for a large-scale annotation project involving multiple datasets (images, text, and sensor data). While the platform works great overall, I’ve noticed some performance slowdowns when switching between datasets or loading large batches of data.

I’m wondering — how do you all manage multiple datasets efficiently in Basic.ai? Do you split projects into smaller subsets, or are there best practices for optimizing dataset structure to reduce lag?

Any tips on how to organize or pre-process data for smoother performance would be really appreciated. Thanks in advance!

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