etsi-watchdog 0.2.2

Real-time data drift detection for machine learning pipelines.

etsi-watchdog

Real-time data drift detection for machine learning pipelines.

etsi-watchdog is a production-ready Python library for drift detection, version comparison, and real-time monitoring of data streams. Designed for ML practitioners, data scientists, and AI engineers who need reliable data quality insights.

Features

• PSI-based Drift Detection (more algorithms coming)

• Rolling Monitoring with time-frequency windowing

• Version Drift Comparison between model/data snapshots

• Built-in Visualization & JSON Export

• Minimal Dependencies & Fast Performance

• Clear API, suitable for both research and production

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Installation

pip install etsi-watchdog

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Quickstart

Drift Detection

from etsi.watchdog import DriftCheck
import pandas as pd

ref = pd.read_csv("reference.csv")
live = pd.read_csv("current.csv")

check = DriftCheck(ref)
results = check.run(live, features=["age", "salary"])

for feat, result in results.items():
    print(result.summary())
    result.plot()

Rolling Monitoring

from etsi.watchdog import Monitor

monitor = Monitor(reference_df=ref)
monitor.enable_logging("logs/rolling_log.csv")

results = monitor.watch_rolling(
    df=live_data_stream,
    window=50,
    freq="D",
    features=["age", "salary"]
)

Drift Comparison (A/B)

from etsi.watchdog import DriftComparator

check = DriftCheck(ref)
v1 = check.run(live1, features=["age", "salary"])
v2 = check.run(live2, features=["age", "salary"])

comp = DriftComparator(v1, v2)
print(comp.diff())

Contributing

Contributions are welcome. If you have an idea for a drift metric, integration, or improvement, please open an issue or PR on GitHub.