ML Pipeline Monitor: A Comprehensive OpenTelemetry-Based Observability Platform for Production Machine Learning Systems
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I2P112Keywords:
Machine Learning Operations, MLOps, Observability, Data Drift Detection, Model Monitoring, OpenTelemetry, Production ML SystemsAbstract
As machine learning (ML) systems transition from research prototypes to production deployments, the need for comprehensive monitoring and observability becomes critical. Traditional application monitoring tools fail to address ML-specific challenges such as data drift, model performance degradation, and concept drift. I present ML Pipeline Monitor, a production-ready observability platform built on OpenTelemetry standards that provides end-to-end monitoring for ML pipelines. The system integrates with major cloud ML platforms (AWS SageMaker, Azure ML, Google Cloud Vertex AI) and ML frameworks (PyTorch, TensorFlow, MLflow) to collect multi-dimensional telemetry data including model performance metrics, data quality indicators, and infrastructure health. I implement statistical drift detection algorithms (Kolmogorov–Smirnov test, Population Stability Index, Chi-square test) and anomaly detection methods (Isolation Forest, One-Class SVM) to identify degradation in real-time. Through evaluation on production-style workloads, I demonstrate that the system detects model drift with 94% accuracy, identifies performance anomalies with < 5% false positive rate, and adds < 1ms overhead to prediction latency. The system monitors pipelines processing 10,000+ predictions per second while maintaining < 5% CPU overhead. I provide an open-source implementation for local, cloud, and multi-cloud deployments.
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