Production Artifacts

This tutorial shows the minimum production loop around a fitted Mixle model: record provenance, register a version, serve scores, and run a drift check. It is intentionally modest. Mixle does not replace your deployment platform; it provides model metadata and verification objects that are explicit enough to put behind one.

1. Fit With Provenance

fit_with_provenance returns a fitted model plus a header containing hashes, training settings, timing, environment information, and lineage metadata.

import numpy as np
from mixle.inference.production import fit_with_provenance, verify_lineage
from mixle.stats import GaussianEstimator

rng = np.random.RandomState(0)
data = rng.normal(3.0, 2.0, 4000).tolist()

model, header = fit_with_provenance(
    data,
    GaussianEstimator(),
    max_its=30,
    seed=1,
    out=None,
)

print(header.dataset_hash)
print(header.model_hash)
print(verify_lineage(header))

Keep the header next to the model artifact. It is the record that answers “what data and settings produced this object?”

2. Register A Version

The registry is a lightweight local artifact registry. It is suitable for tests, demos, and simple deployments; larger systems can store the same model and header in their own registry.

from mixle.inference.production import Registry

registry = Registry("/tmp/mixle-demo-registry")
version = registry.register(
    model,
    "demo-gaussian",
    header=header,
    metadata={"owner": "ml-platform", "purpose": "density-monitoring"},
)
registry.promote("demo-gaussian", version, alias="production")

Promotion should happen only after held-out scoring and any domain-specific review gates have passed.

3. Serve Scores

Service is a small scoring wrapper. It records recent calls, exposes a health summary, and keeps scoring behavior close to the fitted distribution.

from mixle.inference.production import Service

service = Service(model, name="demo-gaussian", reference=data)
current = rng.normal(3.0, 2.0, 100).tolist()
log_probs = service.score(current)

print(log_probs[:5])
print(service.health())

Use the service boundary to standardize logging and monitoring. Do not hide model exceptions; failures are part of the operational signal.

4. Detect Drift

Drift checks compare reference data with current data under both feature-level and model-score diagnostics where available.

from mixle.inference.production import detect_drift

shifted = rng.normal(9.0, 2.0, 500).tolist()
report = detect_drift(model, data, shifted)

print(report.drift)
print(report.score)
print(report.per_feature)
print(report.thresholds)

A drift report is not an automatic rollback command. Treat it as evidence for a policy: alert, shadow a challenger, collect labels, retrain, or escalate to manual review.

Operational Checklist

For a production artifact, keep:

• the fitted model;

• the provenance header;

• the training and validation data hashes;

• the package version and optional dependency set;

• the promotion decision and reviewer;

• recent score distributions and drift reports.

Read Production Workflows for the full production API and Model Lifecycle for the higher-level mixle.Model wrapper.