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XGBoost¶
XGBoost (eXtreme Gradient Boosting) is a scalable, widely used open-source gradient-boosted decision-tree library authored by Tianqi Chen. It is a common choice for production tabular classification and regression where tree ensembles outperform deep learning — Zalando Payments' deferred-payment risk-scoring being one such instance.
Seen in¶
- Yelp — ML based ranking using Nrtsearch (2026-05-11). XGBoost is one of the model types Yelp's Nrtsearch Inference Plugin scores documents with — packaged as MLeap bundles and run in-process on search-replica nodes for co-located ranking.
Stub page. Expand as dedicated sources arrive.
Related¶
- systems/mleap — serialization format used to serve XGBoost at Yelp
- systems/nrtsearch — runs XGBoost in-process via the Inference Plugin
- patterns/co-located-inference-in-serving-layer
Wiki positioning¶
- Named as one of the interchangeable "main model" containers in a SageMaker Inference Pipeline Model alongside PyTorch and TensorFlow (Source: ).
- Available as a pre-built SageMaker container — Zalando explicitly calls out that the training step can be swapped for any SageMaker-provided algorithm image, with XGBoost being the canonical first choice.
- Zalando had already ingested XGBoost into their production stack in a 2020 blog post on distributed XGBoost on SageMaker; the 2021 real-time inference post references it as an existing in-house option.
Seen in¶
- — named as one of three framework options ("XGBoost, PyTorch") for the main-model container stage of the SageMaker Inference Pipeline Model used for deferred-payment fraud / default risk scoring. Zalando Payments previously published on distributed XGBoost on SageMaker in 2020 (referenced in-line but not ingested separately).