A system for monitoring statistical data distribution shifts in distributed settings
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Updated
Dec 18, 2023 - Jupyter Notebook
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data-drifts
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This project builds a production-grade ML pipeline to classify Near-Earth Objects (NEOs) as hazardous or non-hazardous. It automates data ingestion, preprocessing, model training, monitoring, and drift detection using GitHub Actions, PostgreSQL, MLflow, DAGsHub, and Grafana.
data-science machine-learning-algorithms postgresql data-visualization statistical-analysis grafana-dashboard chi-square mlops mlflow-tracking-server github-actions population-stability-index dagshub data-drifts data-drift-detection
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Updated
Nov 24, 2025 - Python
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