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import time import random from collections import deque # Simulated model prediction function def model_predict(input_data): # Simulate prediction latency time.sleep(random.uniform(0.01, 0.05)) # Simulate prediction output return random.choice([0, 1]) # Monitoring class to track metrics class ModelMonitor: def __init__(self, window_size=100): self.predictions = deque(maxlen=window_size) self.labels = deque(maxlen=window_size) self.latencies = deque(maxlen=window_size) def log_prediction(self, prediction, label, latency): self.predictions.append(prediction) self.labels.append(label) self.latencies.append(latency) def compute_accuracy(self): if not self.labels: return None correct = sum(p == l for p, l in zip(self.predictions, self.labels)) return correct / len(self.labels) def compute_avg_latency(self): if not self.latencies: return None return sum(self.latencies) / len(self.latencies) def alert_if_needed(self): accuracy = self.compute_accuracy() avg_latency = self.compute_avg_latency() if accuracy is not None and accuracy < 0.7: print(f"ALERT: Accuracy dropped below threshold: {accuracy:.2f}") if avg_latency is not None and avg_latency > 0.04: print(f"ALERT: Latency too high: {avg_latency:.3f} seconds") # Simulate streaming predictions with monitoring monitor = ModelMonitor(window_size=50) for i in range(200): input_data = i # dummy input true_label = random.choice([0, 1]) # simulated true label start_time = time.time() pred = model_predict(input_data) latency = time.time() - start_time monitor.log_prediction(pred, true_label, latency) if i % 20 == 0 and i > 0: acc = monitor.compute_accuracy() avg_lat = monitor.compute_avg_latency() print(f"After {i} predictions - Accuracy: {acc:.2f}, Avg Latency: {avg_lat:.3f} sec") monitor.alert_if_needed()
Before: No metrics collected, no visibility into model performance.
After: Accuracy and latency metrics are tracked and printed every 20 predictions. Alerts notify when performance degrades.
up{job="api-server"} == 1, what does it show?up is 1 when a target is up (running), 0 if down. The filter {job="api-server"} selects only api-server jobs.up == 1, so it shows api-server jobs currently running.up == 1 means service is running [OK]error parsing query: unexpected token. What is the most likely cause?