etsi-watchdog 3.0.0

Real-time data drift detection for machine learning pipelines.

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.

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✨ 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()

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🔹 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"]
)

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🔹 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())

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🚀 etsi-watchdog v3 — Roadmap & Vision

Feature Area	Description
Multi-Algorithm Support	Support for additional drift metrics: - Jensen–Shannon Divergence (JSD) - Wasserstein Distance - Kolmogorov–Smirnov Test (K-S) - Tree-based drift (e.g., DeepDrift)
Plug-in Architecture	Drift algorithms will be fully plug-and-play. Custom metric support via register_drift_function() API.
Real-Time Stream Hooks	Support for Kafka/Redis/WebSockets to detect drift on live data streams.
Concept Drift Detection	Integration with models to detect label or concept drift, not just feature distribution shift.
CLI & YAML Configs	Full CLI support: etsi-watchdog detect --ref ref.csv --live live.csv --features age salary + YAML-based configuration for automated pipelines.
Benchmark Suite	Built-in benchmarking with synthetic datasets to evaluate metric sensitivity and response time.
Dashboard UI (Optional)	Lightweight dashboard (Streamlit/FastAPI) for monitoring drift over time visually.
Drift Summary Reports	Generate PDF/HTML reports with drift summary, top features, histograms, and timestamps.
Sklearn & Pandas Integration	DriftCheck will support .fit()/.transform() methods like Scikit-learn transformers.

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🤝 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.

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🧾 License

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

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👨‍💻 Maintainers

• @PRIYANSH

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