Xalorra
Models/XGBoost

XGBoost in Xalorra

High-signal tabular ML with operational discipline: versioned datasets, versioned models, metrics/leaderboards, and stable predict endpoints. No black box.

Open StudioView docs
ML model (Core)
XGBoost
Versioned training + stable serving in Xalorra
XGBoost logo
Train (Core)Metrics & leaderboardStable predict endpoint
This is classical ML (Core), not a hosted foundation model. Xalorra version-controls dataset scope, artifacts, and metrics—so results stay explainable later.

Training

Train XGBoost on a versioned dataset scope. Xalorra persists run identifiers, artifacts, and metrics so the result stays explainable later.

Dataset-scoped training
Train XGBoost against a specific dataset version (tenant + namespace + dataset + version_label). That scope is persisted with the model version—no ambiguity later.
Metrics and leaderboard-ready
Store evaluation metrics per run so you can compare versions and promote the right one. No more “which run was this?” guessing.
Artifacts you can retrieve later
Persist model artifacts and evaluation outputs tied to stable identifiers. When something breaks, you have the evidence—not just a screenshot.
Boundary you should keep
This is core ML. Xalorra is a control plane: it orchestrates training, stores versions, metrics, and artifacts, and exposes stable endpoints. It’s not an AutoML black box.

Ship ML you can defend later.

Dataset scope, model versions, metrics, and artifacts—kept coherent across tenants and environments.

Get startedContact sales