Monitor in g a PyTorch Model in Production: Latency Tracking, Data Drift, Prediction Drift, and Alerting

Accuracy metrics are computed offline; production metrics matter online. Track latency, throughput, error rates, and data drift. This post covers monitoring infrastructure, drift detection, and alerting rules.

Latency Tracking

import time
from collections import deque
class LatencyMonitor: def __init__(self, window_size=1000): self.latencies = deque(maxlen=window_size)
def record(self, latency_ms): self.latencies.append(latency_ms)
def p99_latency(self): sorted_lats = sorted(self.latencies) return sorted_lats[int(0.99 * len(sorted_lats))]
monitor = LatencyMonitor() # Record latencies during inference monitor.record(25) monitor.record(30) print(f"P99 latency: {monitor.p99_latency()}ms") 

Conclusion

Production monitoring detects issues early. Latency, throughput, drift, and error rates provide comprehensive visibility. Building monitoring infrastructure separates hobbyist ML from production-grade systems. This concludes the PyTorch Mastery series.