etsi-watchdog

📄 DOCS

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

📦 Installation

pip install etsi-watchdog

⚡ 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())

🚀 etsi-watchdog v3 — Roadmap & Vision

🤝 Contributing

Contributions are welcome! 🙌
If you have an idea for a drift metric, integration, or improvement, feel free to open an issue or pull request.

Please refer to CONTRIBUTING.md for guidelines.

🧾 License

Licensed under the BSD-2-Clause License.
See the LICENSE file for details.

👨‍💻 Maintainers

• @PRIYANSH