Prometheus Alertmanager for ML platforms
Design Prometheus alerts and Alertmanager routing for ML platforms without noisy pages, missing owners, or unsafe high-cardinality labels.
Prometheus and Alertmanager have different responsibilities. Prometheus evaluates alerting rules over time series. Alertmanager receives firing alerts and handles grouping, routing, inhibition, silences, and notification delivery.
That boundary matters. Alertmanager can reduce duplicate notifications, but it cannot repair a noisy condition or a poorly chosen PromQL expression. Effective alerting begins with an actionable signal.
Follow the alert path
The official Alertmanager documentation describes the notification pipeline:
- Prometheus evaluates an alert expression.
- A
forduration can require the condition to persist. - Prometheus sends firing and resolved alerts to Alertmanager.
- Alertmanager groups related alerts, applies inhibition or silences, selects a routing branch, and notifies receivers.
Each step needs its own health checks. A correct rule is useless when Prometheus cannot reach Alertmanager. A healthy Alertmanager is ineffective when a route has no valid receiver.
Run at least two compatible Alertmanager instances for production availability and configure Prometheus with every instance rather than placing a load balancer in front as if alerts were ordinary stateless requests.
Alert on outcomes and sustained risk
ML infrastructure exposes many interesting metrics but relatively few page-worthy conditions. Separate three levels:
- Page: users cannot submit, start, complete, or serve important workloads within an agreed objective.
- Ticket or notification: capacity, failure rate, or component degradation needs action during normal work.
- Dashboard: useful context that does not independently require a response.
For example, one Pending Pod is usually diagnostic context. A sustained loss of eligible GPU capacity that causes priority workloads to miss start-time objectives may deserve a page.
Use the for field to avoid alerting on ordinary short reconciliations. Choose the duration from the workload objective, not from a generic rule pack.
Create stable routing labels
Alertmanager routes on labels. Standardize a bounded set such as severity, team, service, environment, cluster, and workload_class. Make ownership available before the incident.
Do not put unique run IDs, Pod UIDs, user names, prompt contents, or artifact paths into broad metric labels. Those dimensions can create high cardinality, reveal sensitive data, and make grouping ineffective. Keep precise identifiers in annotations, logs, traces, or Polyaxon run links.
Annotations should explain the symptom, measured value, impact, and next diagnostic step. Include a runbook or dashboard URL whose permissions match the audience.
Group and inhibit deliberately
Grouping turns many related alerts into one notification. Group by the dimensions that describe a shared incident, such as cluster, service, and alert name. Grouping by Pod often preserves the flood you intended to remove.
Inhibition suppresses lower-level alerts when a broader failure already explains them. If a node is unavailable, individual container symptoms on that node may add little value. If the telemetry pipeline is failing, be cautious about suppressing application alerts: missing data can make downstream conditions disappear entirely.
Silences are temporary operational controls, not permanent fixes. Require an owner and end time, and review long-lived or repeated silences as alert-quality problems.
Alert across the ML execution path
Cover the boundaries that can prevent useful work:
| Layer | Example actionable condition |
|---|---|
| Platform API | Sustained errors or latency prevent submissions |
| Queue and admission | Priority workloads exceed start-time objective |
| Kubernetes | Eligible capacity falls or critical controllers stop reconciling |
| Node and accelerator | Device loss, memory errors, or pressure reduces usable capacity |
| Storage and network | Artifact or dataset access blocks a workload class |
| Workload | Failure or retry rate exceeds the expected baseline |
| Model service | Availability, latency, or quality guardrail is violated |
| Telemetry | Prometheus targets or Alertmanager delivery paths are unavailable |
Correlate resource alerts with workload outcomes. High GPU utilization can be healthy; low utilization can be acceptable during a planned idle period. Alert when the condition threatens an objective or represents an owned failure mode.
Test rules and delivery
Review rules as code and test their expressions against representative data before rollout. Exercise the entire delivery chain in each environment: rule evaluation, network path, routing, grouping, receiver authentication, resolution, and escalation.
Monitor Prometheus rule evaluation and Alertmanager notification errors. Create an external check for the alerting pipeline so a failure of the monitoring system does not hide itself.
Track alert volume, repeated pages, manual silences, acknowledgements, and actionable outcomes. Delete or downgrade alerts that consistently require no action.
Link alerts to Polyaxon context
Polyaxon platform observability exposes the operational context of the services managing ML workloads. Run logging and metadata help connect a platform symptom to the exact operation, configuration, and artifacts involved.
Route shared-infrastructure alerts to platform owners and workload-specific failures to the team that can act. Do not expose another tenant's identifiers or logs through a broadly delivered notification.
Prometheus should decide when a meaningful condition is true. Alertmanager should deliver that condition to the right owner with controlled noise. Keeping those roles clear produces an alerting system that helps teams recover rather than adding another incident to manage.