Xalorra
Models/Logistic Regression

Logistic Regression in Xalorra

Classical ML, done the boring-critical way: versioned datasets, versioned models, tenant-safe artifacts, and stable predict endpoints. No black box.

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ML model (Core)
Logistic Regression
Versioned training + stable serving in Xalorra
Logistic Regression logo
Train (Core)Versioned artifactsStable predict endpoint
This is classical ML (Core), not a hosted foundation model. Xalorra version-controls the dataset scope, model artifacts, and metrics—so you can audit results later.

Training

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

Dataset-scoped training
Train LogReg against a specific dataset version (tenant + namespace + dataset + version_label). That scope is persisted with the model version—no ambiguity later.
Versioned artifacts and metrics
Each run stores stable identifiers, metrics, and artifacts tied to the model version. You can compare versions without guessing which data produced them.
Tenant-safe by design
Everything is tenant-aware: data access, run records, artifacts, and model resolution. No cross-tenant leakage, no “oops” pipelines.
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.

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