What is Model Health Check?

Success metrics include system uptime, model performance stability, deployment velocity, and operational cost efficiency.

Implement five health check layers: liveness (model process is running and accepting connections, checked every 10-30 seconds), readiness (model is fully loaded into memory and warm, feature store connections are active, all preprocessing dependencies are available), prediction quality (send standardized canary inputs with known expected outputs every 5 minutes, verifying predictions fall within acceptable ranges), resource health (GPU memory usage below 90%, CPU utilization within expected bounds, disk space sufficient for logging), and dependency health (feature store responding within latency targets, upstream data pipelines running on schedule, monitoring systems receiving metrics). Expose health status through standardized endpoints (/health, /ready, /live) consumed by Kubernetes probes and load balancers. Aggregate health signals into a single model health score displayed on operations dashboards.

Deploy three proactive detection mechanisms: canary prediction testing (send 10-20 known input-output pairs through the model every 5 minutes, alerting when any prediction deviates beyond tolerance thresholds, catching model corruption or loading errors), statistical output monitoring (compare the distribution of production predictions over rolling 1-hour windows against the expected distribution from training, alerting when Jensen-Shannon divergence exceeds 0.05), and performance trend analysis (track accuracy on a daily labeled sample, latency percentile trends, and error rate moving averages, alerting on negative trends before they breach absolute thresholds). These mechanisms detect degradation 2-10x faster than waiting for user complaints or business metric impact. Implement using Prometheus custom metrics with Grafana alerting for cost-effective monitoring.

Implement five health check layers: liveness (model process is running and accepting connections, checked every 10-30 seconds), readiness (model is fully loaded into memory and warm, feature store connections are active, all preprocessing dependencies are available), prediction quality (send standardized canary inputs with known expected outputs every 5 minutes, verifying predictions fall within acceptable ranges), resource health (GPU memory usage below 90%, CPU utilization within expected bounds, disk space sufficient for logging), and dependency health (feature store responding within latency targets, upstream data pipelines running on schedule, monitoring systems receiving metrics). Expose health status through standardized endpoints (/health, /ready, /live) consumed by Kubernetes probes and load balancers. Aggregate health signals into a single model health score displayed on operations dashboards.

Deploy three proactive detection mechanisms: canary prediction testing (send 10-20 known input-output pairs through the model every 5 minutes, alerting when any prediction deviates beyond tolerance thresholds, catching model corruption or loading errors), statistical output monitoring (compare the distribution of production predictions over rolling 1-hour windows against the expected distribution from training, alerting when Jensen-Shannon divergence exceeds 0.05), and performance trend analysis (track accuracy on a daily labeled sample, latency percentile trends, and error rate moving averages, alerting on negative trends before they breach absolute thresholds). These mechanisms detect degradation 2-10x faster than waiting for user complaints or business metric impact. Implement using Prometheus custom metrics with Grafana alerting for cost-effective monitoring.

Implement five health check layers: liveness (model process is running and accepting connections, checked every 10-30 seconds), readiness (model is fully loaded into memory and warm, feature store connections are active, all preprocessing dependencies are available), prediction quality (send standardized canary inputs with known expected outputs every 5 minutes, verifying predictions fall within acceptable ranges), resource health (GPU memory usage below 90%, CPU utilization within expected bounds, disk space sufficient for logging), and dependency health (feature store responding within latency targets, upstream data pipelines running on schedule, monitoring systems receiving metrics). Expose health status through standardized endpoints (/health, /ready, /live) consumed by Kubernetes probes and load balancers. Aggregate health signals into a single model health score displayed on operations dashboards.

Deploy three proactive detection mechanisms: canary prediction testing (send 10-20 known input-output pairs through the model every 5 minutes, alerting when any prediction deviates beyond tolerance thresholds, catching model corruption or loading errors), statistical output monitoring (compare the distribution of production predictions over rolling 1-hour windows against the expected distribution from training, alerting when Jensen-Shannon divergence exceeds 0.05), and performance trend analysis (track accuracy on a daily labeled sample, latency percentile trends, and error rate moving averages, alerting on negative trends before they breach absolute thresholds). These mechanisms detect degradation 2-10x faster than waiting for user complaints or business metric impact. Implement using Prometheus custom metrics with Grafana alerting for cost-effective monitoring.