Build an ML Observability Infrastructure

May, 2026 • Alex Serban, Koen van der Blom, Joost Visser

41 / 57 • Deployment •

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Intent

Make the internal state of ML systems queryable and actionable in production.

Motivation

Logging individual predictions is necessary but not sufficient. Without aggregating and correlating logs, metrics, and traces into a coherent observability stack, it is hard to diagnose degradations, understand failure modes, or satisfy audit requirements at scale.

Applicability

An observability infrastructure should be established for any production-grade ML system that requires reliability or accountability.

Description

ML observability goes beyond logging: it is the capacity to understand what a model is doing, why, and when something is wrong, based on the signals the system emits.

A mature ML observability infrastructure includes three complementary pillars:

Metrics and Dashboards

Aggregate model and data metrics into a centralised dashboard. Track performance KPIs such as accuracy, latency and throughput alongside business-level outcomes. Establish baselines at deployment time and make drift visible at a glance. Dashboards should cover at minimum: model quality metrics, data pipeline health, and infrastructure resource usage.

Structured and Correlated Logs

Ensure that prediction logs, data pipeline events, and system logs share common identifiers (request ID, model version, data batch ID) so that events can be correlated across the serving stack. This enables end-to-end tracing from an incoming request to the model version and training data that produced the response, which is a prerequisite for incident investigation and compliance audits.

Alerting and On-Call Integration

Define alert thresholds for key metrics and integrate them with the team’s incident management workflow. Alerts should distinguish between critical model degradation requiring immediate rollback and slower-moving trends such as gradual data drift. Avoid alert fatigue by ensuring each alert is actionable and routed to the responsible owner.

Observability signals should be retained for a period sufficient for post-incident analysis and regulatory review, and tied to the versioned model and data artefacts that produced them.