dataenginex.ml¶

Classical ML — training, model registry, drift detection, and model serving.

LLM providers, vector stores, agents, and RAG live in dataenginex.ai. The drift scheduler lives in dataenginex.orchestration.

Module Split¶

Concern	Module
Training, registry, serving, drift	`dataenginex.ml`
LLM providers, chat, embeddings	`dataenginex.ai.llm`
Vector stores	`dataenginex.ai.vectorstore`
Background drift scheduling	`dataenginex.orchestration.scheduler`

Quick Usage¶

from dataenginex.ml import (
    SklearnTrainer, TrainingResult,
    ModelRegistry, ModelArtifact, ModelStage,
    DriftDetector, DriftReport,
    ModelServer, PredictionRequest, PredictionResponse,
)

# Train
trainer = SklearnTrainer(experiment_name="churn")
result: TrainingResult = trainer.train(X_train, y_train)

# Register
registry = ModelRegistry()
registry.register(result.model, name="churn_v1", stage=ModelStage.STAGING)

# Drift
detector = DriftDetector(reference=X_train)
report: DriftReport = detector.detect(X_new)

# Serve
server = ModelServer()
server.load("churn_v1", stage=ModelStage.PRODUCTION)
resp = server.predict(PredictionRequest(features={"age": 35}))

::: dataenginex.ml