ML Monitoring vs. ML Observability

Machine learning (ML) monitoring and observability are crucial for developing reliable ML models. Monitoring tracks a model's performance, behavior, and health from development to production, while observability provides insights into the inner workings of ML data pipelines and system well-being. Both practices aim to ensure optimal functionality and prompt detection of potential issues or anomalies in ML systems. ML monitoring involves continuous observation, analysis, and management of various aspects of ML systems to ensure they are functioning as intended and delivering accurate outcomes. Key objectives include model performance tracking, early anomaly detection, root-cause analysis, diagnosis, model governance, compliance, proactive anomaly resolution, data drift detection, continuous improvement, risk mitigation, and performance validation. ML observability provides insights into the inner workings of ML data pipelines and system well-being. Its primary objectives are transparency and understandability, root cause analysis, data quality assessment, and performance optimization. Observability offers real-time decision support, builds trust in AI systems, and ensures compliance and accountability. Both monitoring and observability share common goals, such as anomaly detection, data quality control, real-time alerts, continuous ML improvement, and model performance assessment. However, they differ significantly in their focus, objectives, approach, perspective, and performance analytics. Encord Active is an open-source ML platform that offers comprehensive monitoring and observability features to help practitioners develop robust ML models. By leveraging both practices, organizations can build reliable and trustworthy AI-driven solutions and drive innovation.