stayready 0.2.0

Push drift events and audit entries from your MLOps pipeline to Compass StayReady.

StayReady Python SDK

Push drift events and audit entries from your MLOps pipeline to Compass StayReady — continuous AI governance monitoring for regulated sectors.

Install

pip install stayready

Quick start

import os
from stayready import StayReady

sr = StayReady(
    api_key=os.environ["STAYREADY_API_KEY"],
    model_id="your-model-uuid",  # from your StayReady dashboard
)

# Report drift when your monitoring detects it — StayReady computes severity
# itself from value/threshold/direction and this metric's own history; you
# don't set it.
sr.drift(
    type="data_drift",              # data_drift | concept_drift | performance_degradation | regulatory_change
    metric="PSI",
    value=0.28,
    threshold=0.20,
    description="Population Stability Index exceeded threshold on income feature.",
    affected_domains=["Data Quality", "Model Monitoring"],
    action_required="Retrain on recent data and re-validate before next production cycle.",
)

# Log lifecycle events to the immutable audit trail
sr.audit("model_retrained", "Retrained on Q2 data, Gini improved to 0.61", actor="ml-pipeline")

Severity is computed server-side by comparing value against threshold and this model+metric's own trailing baseline — not something you set. Critical and High results trigger an email alert (and webhook, if configured), deduped so repeated near-identical events don't re-notify, and can flag your linked GovernReady audit for re-assessment.

Most metrics are "higher is worse" (PSI, KS, error rate) — the default. If a drop is what's bad for your metric (Gini, accuracy, F1), pass direction="lower_is_worse" on the first call you ever make for that model+metric; it registers the metric's polarity and is ignored on later calls, so you can't accidentally flip it mid-stream.

Integration examples

Evidently AI

psi = report.as_dict()["metrics"][0]["result"]["dataset_drift_share"]
sr.drift(type="data_drift", metric="PSI", value=psi,
         threshold=0.2, description="Evidently reported this drift share.")

Airflow (post-training validation)

def report_validation(**ctx):
    gini = ctx["ti"].xcom_pull(key="gini")
    sr.drift(type="performance_degradation", metric="Gini", value=gini,
             threshold=0.55, direction="lower_is_worse",
             description="Gini after retrain.")

Requirements

Python 3.9+. Zero dependencies (standard library only).

Support

[e-mail] · stayready.dendrons.ai