NVIDIA-Merlin / systems

Merlin Systems provides tools for combining recommendation models with other elements of production recommender systems (like feature stores, nearest neighbor search, and exploration strategies) into end-to-end recommendation pipelines that can be served with Triton Inference Server.
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[BUG] Robust 2-stage recommender system pipeline #207

Open bschifferer opened 1 year ago

bschifferer commented 1 year ago

Bug description

The unit test of the 2-stage recommender system pipeline is shaky due to multiple reasons:

Unit test: https://github.com/NVIDIA-Merlin/Merlin/blob/main/tests/unit/examples/test_building_deploying_multi_stage_RecSys.py

Edge cases, we should be handling without crashing the systems:

What should be the result in each of the cases?

rnyak commented 1 year ago

thanks @bschifferer. these are all valid points. can we also add nulls issue to this list? integration test fails if we have nulls in the user id and item id columns in the real dataset.

viswa-nvidia commented 1 year ago

Changed priority to P1. Refer https://nvidia.slack.com/archives/C01RP7T89PY/p1663872124879779?thread_ts=1663843219.331779&cid=C01RP7T89PY

karlhigley commented 1 year ago

Just for context on how we got here:

I agree that this stuff is important though, and we might soon have bandwidth to tackle it, once we get session-based serving for both TF and Torch ironed out. Maybe in the 23.04-23.05 timeline?