Production edge AI deployments require comprehensive operational infrastructure managing distributed device fleets, ensuring consistent performance, detecting degradation, and enabling safe model updates. This final post in the series covers production operations at scale including Prometheus and Jaeger integration for distributed monitoring, data drift detection mechanisms identifying model degradation, model versioning strategies with semantic versioning and registry management, canary deployment patterns enabling safe rollouts, OTA update procedures for zero-downtime upgrades, comprehensive health checking across device fleets, feedback loop implementation for continuous improvement, and orchestration patterns managing 100+ distributed edge devices.
Part 5 covered advanced optimization for maximizing edge performance. This concluding post addresses operational maturity: implementing observability for distributed systems, detecting when models require retraining, managing model lifecycles across device fleets, safely deploying updates without service interruption, monitoring device health proactively, and establishing feedback mechanisms enabling continuous model improvement based on production data.
Distributed Monitoring with Prometheus and Jaeger
Observability across distributed edge deployments requires standardized metrics collection, distributed tracing, and centralized aggregation enabling fleet-wide visibility.
flowchart TD
A[Edge Device 1] --> B[Prometheus Exporter]
C[Edge Device 2] --> D[Prometheus Exporter]
E[Edge Device N] --> F[Prometheus Exporter]
B --> G[Prometheus Server]
D --> G
F --> G
G --> H[Grafana Dashboard]
A --> I[Jaeger Agent]
C --> J[Jaeger Agent]
E --> K[Jaeger Agent]
I --> L[Jaeger Collector]
J --> L
K --> L
L --> M[Jaeger Storage]
M --> N[Jaeger UI]
G --> O[Alertmanager]
O --> P[Notifications]
H --> Q[Operations Team]
N --> Q
P --> QPrometheus Metrics Exporter (Python):
#!/usr/bin/env python3
"""
Prometheus metrics exporter for edge inference service
"""
from prometheus_client import start_http_server, Gauge, Counter, Histogram, Info
import time
import psutil
import subprocess
import torch
class EdgeMetricsExporter:
"""
Export comprehensive edge device metrics to Prometheus
"""
def __init__(self, device_id, port=8000):
self.device_id = device_id
self.port = port
# Device information
self.device_info = Info('edge_device', 'Edge device information')
self.device_info.info({
'device_id': device_id,
'platform': self.get_platform_info(),
'cuda_version': torch.version.cuda or 'N/A'
})
# Inference metrics
self.inference_latency = Histogram(
'inference_latency_seconds',
'Inference latency in seconds',
buckets=[0.01, 0.02, 0.03, 0.05, 0.1, 0.2, 0.5, 1.0]
)
self.inference_requests = Counter(
'inference_requests_total',
'Total inference requests',
['model', 'status']
)
self.active_requests = Gauge(
'inference_active_requests',
'Currently active inference requests'
)
self.queue_depth = Gauge(
'inference_queue_depth',
'Inference request queue depth'
)
# GPU metrics
self.gpu_utilization = Gauge(
'gpu_utilization_percent',
'GPU utilization percentage'
)
self.gpu_memory_used = Gauge(
'gpu_memory_used_bytes',
'GPU memory used in bytes'
)
self.gpu_memory_total = Gauge(
'gpu_memory_total_bytes',
'Total GPU memory in bytes'
)
self.gpu_temperature = Gauge(
'gpu_temperature_celsius',
'GPU temperature in Celsius'
)
self.gpu_power = Gauge(
'gpu_power_watts',
'GPU power consumption in watts'
)
# System metrics
self.cpu_utilization = Gauge(
'cpu_utilization_percent',
'CPU utilization percentage'
)
self.memory_used = Gauge(
'memory_used_bytes',
'System memory used in bytes'
)
self.memory_total = Gauge(
'memory_total_bytes',
'Total system memory in bytes'
)
self.disk_used = Gauge(
'disk_used_bytes',
'Disk space used in bytes'
)
# Model metrics
self.model_loaded = Gauge(
'model_loaded',
'Model load status (1=loaded, 0=not loaded)',
['model_name', 'version']
)
self.model_load_time = Histogram(
'model_load_time_seconds',
'Model loading time in seconds'
)
# Health metrics
self.device_health = Gauge(
'device_health_status',
'Device health status (1=healthy, 0=unhealthy)'
)
def get_platform_info(self):
"""Get Jetson platform information"""
try:
result = subprocess.run(
['cat', '/proc/device-tree/model'],
capture_output=True,
text=True
)
return result.stdout.strip()
except:
return 'Unknown'
def update_gpu_metrics(self):
"""Update GPU metrics"""
try:
if torch.cuda.is_available():
# Memory
free_memory, total_memory = torch.cuda.mem_get_info()
used_memory = total_memory - free_memory
self.gpu_memory_used.set(used_memory)
self.gpu_memory_total.set(total_memory)
# Utilization (requires nvidia-smi or jetson_stats)
try:
result = subprocess.run(
['nvidia-smi', '--query-gpu=utilization.gpu,temperature.gpu,power.draw',
'--format=csv,noheader,nounits'],
capture_output=True,
text=True
)
if result.returncode == 0:
util, temp, power = result.stdout.strip().split(',')
self.gpu_utilization.set(float(util))
self.gpu_temperature.set(float(temp))
self.gpu_power.set(float(power))
except:
pass
except Exception as e:
print(f"Error updating GPU metrics: {e}")
def update_system_metrics(self):
"""Update system metrics"""
try:
# CPU
cpu_percent = psutil.cpu_percent(interval=1)
self.cpu_utilization.set(cpu_percent)
# Memory
memory = psutil.virtual_memory()
self.memory_used.set(memory.used)
self.memory_total.set(memory.total)
# Disk
disk = psutil.disk_usage('/')
self.disk_used.set(disk.used)
except Exception as e:
print(f"Error updating system metrics: {e}")
def record_inference(self, model_name, latency, success):
"""Record inference metrics"""
self.inference_latency.observe(latency)
status = 'success' if success else 'error'
self.inference_requests.labels(model=model_name, status=status).inc()
def set_queue_depth(self, depth):
"""Set current queue depth"""
self.queue_depth.set(depth)
def set_active_requests(self, count):
"""Set active request count"""
self.active_requests.set(count)
def set_model_status(self, model_name, version, loaded):
"""Set model load status"""
self.model_loaded.labels(model_name=model_name, version=version).set(1 if loaded else 0)
def set_health_status(self, healthy):
"""Set device health status"""
self.device_health.set(1 if healthy else 0)
def start(self):
"""Start metrics HTTP server"""
start_http_server(self.port)
print(f"Metrics server started on port {self.port}")
# Start background update thread
import threading
self.running = True
self.update_thread = threading.Thread(target=self.update_loop)
self.update_thread.daemon = True
self.update_thread.start()
def update_loop(self):
"""Background metrics update loop"""
while self.running:
self.update_gpu_metrics()
self.update_system_metrics()
time.sleep(5) # Update every 5 seconds
def stop(self):
"""Stop metrics exporter"""
self.running = False
# Example usage
if __name__ == '__main__':
exporter = EdgeMetricsExporter(device_id='jetson-001', port=8000)
exporter.start()
# Simulate some metrics
exporter.set_model_status('yolov8n', 'v1.0.0', True)
exporter.set_health_status(True)
for i in range(100):
# Simulate inference
latency = 0.02 + (0.01 * (i % 10) / 10)
success = i % 20 != 0
exporter.record_inference('yolov8n', latency, success)
exporter.set_queue_depth(i % 5)
exporter.set_active_requests(min(i % 8, 4))
time.sleep(0.1)
# Keep running
try:
while True:
time.sleep(1)
except KeyboardInterrupt:
exporter.stop()
Jaeger Distributed Tracing Integration (Python):
#!/usr/bin/env python3
"""
Jaeger distributed tracing for edge inference pipeline
"""
from jaeger_client import Config
from opentracing.ext import tags
from opentracing.propagation import Format
import time
class EdgeTracingService:
"""
Distributed tracing service for edge inference
"""
def __init__(self, service_name, agent_host='localhost', agent_port=6831):
self.service_name = service_name
# Configure Jaeger
config = Config(
config={
'sampler': {
'type': 'const',
'param': 1, # Sample all traces
},
'local_agent': {
'reporting_host': agent_host,
'reporting_port': agent_port,
},
'logging': True,
},
service_name=service_name,
validate=True
)
self.tracer = config.initialize_tracer()
def trace_inference(self, model_name, input_data):
"""Trace complete inference pipeline"""
with self.tracer.start_active_span('inference_request') as scope:
span = scope.span
span.set_tag('model', model_name)
span.set_tag('input_size', len(input_data))
span.set_tag(tags.SPAN_KIND, tags.SPAN_KIND_RPC_SERVER)
try:
# Preprocessing
preprocessed = self.trace_preprocessing(input_data, span)
# Model inference
result = self.trace_model_inference(model_name, preprocessed, span)
# Postprocessing
final_result = self.trace_postprocessing(result, span)
span.set_tag('status', 'success')
span.set_tag('detections', len(final_result))
return final_result
except Exception as e:
span.set_tag('error', True)
span.set_tag('error.message', str(e))
span.log_kv({'event': 'error', 'message': str(e)})
raise
def trace_preprocessing(self, input_data, parent_span):
"""Trace preprocessing step"""
with self.tracer.start_span('preprocessing', child_of=parent_span) as span:
span.set_tag('operation', 'image_preprocessing')
start = time.time()
# Simulate preprocessing
time.sleep(0.002) # 2ms
preprocessed = input_data
duration = time.time() - start
span.set_tag('duration_ms', duration * 1000)
return preprocessed
def trace_model_inference(self, model_name, input_data, parent_span):
"""Trace model inference step"""
with self.tracer.start_span('model_inference', child_of=parent_span) as span:
span.set_tag('model', model_name)
span.set_tag('operation', 'tensorrt_inference')
start = time.time()
# Simulate inference
time.sleep(0.015) # 15ms
result = {'detections': []}
duration = time.time() - start
span.set_tag('duration_ms', duration * 1000)
return result
def trace_postprocessing(self, inference_result, parent_span):
"""Trace postprocessing step"""
with self.tracer.start_span('postprocessing', child_of=parent_span) as span:
span.set_tag('operation', 'nms_filtering')
start = time.time()
# Simulate postprocessing
time.sleep(0.003) # 3ms
final_result = inference_result
duration = time.time() - start
span.set_tag('duration_ms', duration * 1000)
return final_result
def close(self):
"""Close tracer"""
self.tracer.close()
# Example usage
if __name__ == '__main__':
tracing = EdgeTracingService('edge-inference-jetson-001')
for i in range(10):
dummy_input = b'dummy_image_data'
result = tracing.trace_inference('yolov8n', dummy_input)
print(f"Request {i}: {result}")
time.sleep(0.1)
tracing.close()
Data Drift Detection
Production models degrade as input data distributions shift from training distributions. Data drift detection identifies when retraining becomes necessary.
Data Drift Monitor (Python):
#!/usr/bin/env python3
"""
Data drift detection for production edge models
"""
import numpy as np
from collections import deque
from scipy.stats import ks_2samp, wasserstein_distance
import time
class DataDriftMonitor:
"""
Monitor input data distribution for drift detection
"""
def __init__(self, baseline_samples=1000, window_size=500,
drift_threshold=0.1):
self.baseline_samples = baseline_samples
self.window_size = window_size
self.drift_threshold = drift_threshold
# Baseline distribution
self.baseline_features = None
self.baseline_established = False
# Current window
self.current_window = deque(maxlen=window_size)
# Drift history
self.drift_scores = deque(maxlen=1000)
self.drift_alerts = []
# Feature extraction
self.feature_extractors = {
'mean_intensity': self.extract_mean_intensity,
'std_intensity': self.extract_std_intensity,
'brightness': self.extract_brightness,
'contrast': self.extract_contrast,
}
def extract_features(self, image):
"""Extract statistical features from image"""
features = {}
for name, extractor in self.feature_extractors.items():
features[name] = extractor(image)
return features
def extract_mean_intensity(self, image):
"""Mean pixel intensity"""
return np.mean(image)
def extract_std_intensity(self, image):
"""Standard deviation of intensity"""
return np.std(image)
def extract_brightness(self, image):
"""Average brightness"""
return np.mean(image) / 255.0
def extract_contrast(self, image):
"""Contrast measure"""
return np.std(image) / (np.mean(image) + 1e-7)
def establish_baseline(self, images):
"""Establish baseline distribution from training/validation data"""
print(f"Establishing baseline from {len(images)} images...")
baseline_features = {name: [] for name in self.feature_extractors.keys()}
for image in images[:self.baseline_samples]:
features = self.extract_features(image)
for name, value in features.items():
baseline_features[name].append(value)
# Convert to numpy arrays
self.baseline_features = {
name: np.array(values)
for name, values in baseline_features.items()
}
self.baseline_established = True
print("Baseline established")
def add_sample(self, image):
"""Add new sample to current window"""
features = self.extract_features(image)
self.current_window.append(features)
def detect_drift(self):
"""Detect data drift using statistical tests"""
if not self.baseline_established:
return None, "Baseline not established"
if len(self.current_window) < self.window_size:
return None, f"Insufficient samples: {len(self.current_window)}/{self.window_size}"
# Compute drift scores for each feature
drift_results = {}
for feature_name in self.feature_extractors.keys():
baseline = self.baseline_features[feature_name]
current = np.array([sample[feature_name] for sample in self.current_window])
# Kolmogorov-Smirnov test
ks_statistic, ks_pvalue = ks_2samp(baseline, current)
# Wasserstein distance
wasserstein = wasserstein_distance(baseline, current)
drift_results[feature_name] = {
'ks_statistic': ks_statistic,
'ks_pvalue': ks_pvalue,
'wasserstein': wasserstein,
'drifted': ks_statistic > self.drift_threshold
}
# Overall drift score (max KS statistic)
max_ks = max(result['ks_statistic'] for result in drift_results.values())
overall_drift = max_ks > self.drift_threshold
# Record drift score
self.drift_scores.append({
'timestamp': time.time(),
'max_ks': max_ks,
'drifted': overall_drift,
'details': drift_results
})
# Generate alert if drifted
if overall_drift:
alert = {
'timestamp': time.time(),
'severity': 'warning' if max_ks < self.drift_threshold * 1.5 else 'critical',
'max_ks': max_ks,
'affected_features': [
name for name, result in drift_results.items()
if result['drifted']
]
}
self.drift_alerts.append(alert)
return True, drift_results
return False, drift_results
def get_drift_report(self):
"""Generate drift monitoring report"""
if not self.drift_scores:
return {'status': 'No data'}
recent_scores = list(self.drift_scores)[-100:]
max_scores = [score['max_ks'] for score in recent_scores]
return {
'baseline_samples': self.baseline_samples,
'current_window_size': len(self.current_window),
'total_samples_processed': len(self.drift_scores),
'recent_max_ks': {
'mean': np.mean(max_scores),
'max': np.max(max_scores),
'current': recent_scores[-1]['max_ks']
},
'drift_threshold': self.drift_threshold,
'total_alerts': len(self.drift_alerts),
'recent_alerts': self.drift_alerts[-5:]
}
# Example usage
if __name__ == '__main__':
monitor = DataDriftMonitor(
baseline_samples=1000,
window_size=500,
drift_threshold=0.1
)
# Establish baseline with synthetic data
baseline_images = [
np.random.normal(120, 30, (640, 640, 3)).clip(0, 255).astype(np.uint8)
for _ in range(1000)
]
monitor.establish_baseline(baseline_images)
# Simulate production samples (gradually shifting distribution)
for i in range(2000):
# Introduce gradual drift after 1000 samples
if i < 1000:
image = np.random.normal(120, 30, (640, 640, 3)).clip(0, 255).astype(np.uint8)
else:
# Shifted distribution
shift = (i - 1000) / 1000 * 40 # Gradually shift mean
image = np.random.normal(120 + shift, 30, (640, 640, 3)).clip(0, 255).astype(np.uint8)
monitor.add_sample(image)
# Check drift every 500 samples
if i % 500 == 0 and i > 0:
drifted, results = monitor.detect_drift()
if drifted:
print(f"\n⚠ DRIFT DETECTED at sample {i}")
for feature, result in results.items():
if result['drifted']:
print(f" {feature}: KS={result['ks_statistic']:.3f}")
else:
print(f"\n✓ No drift detected at sample {i}")
# Final report
report = monitor.get_drift_report()
print(f"\n{'='*60}")
print("Drift Monitoring Report:")
print(f"Total samples: {report['total_samples_processed']}")
print(f"Total alerts: {report['total_alerts']}")
print(f"Recent max KS: {report['recent_max_ks']['current']:.3f}")
Model Versioning and Registry
Managing model versions across distributed deployments requires centralized registry with semantic versioning and metadata tracking.
Model Registry (Python):
#!/usr/bin/env python3
"""
Model registry for version management
"""
import json
import hashlib
import os
from dataclasses import dataclass, asdict
from typing import List, Optional
from datetime import datetime
@dataclass
class ModelMetadata:
name: str
version: str
architecture: str
framework: str
precision: str
input_shape: tuple
mAP: float
latency_ms: float
model_size_mb: float
training_date: str
checksum: str
tags: List[str]
description: str
class ModelRegistry:
"""
Centralized model registry with versioning
"""
def __init__(self, registry_path='model_registry.json'):
self.registry_path = registry_path
self.models = {}
self.load_registry()
def load_registry(self):
"""Load registry from disk"""
if os.path.exists(self.registry_path):
with open(self.registry_path, 'r') as f:
data = json.load(f)
for model_key, model_data in data.items():
metadata = ModelMetadata(**model_data)
self.models[model_key] = metadata
def save_registry(self):
"""Save registry to disk"""
data = {
key: asdict(metadata)
for key, metadata in self.models.items()
}
with open(self.registry_path, 'w') as f:
json.dump(data, f, indent=2)
def compute_checksum(self, model_path):
"""Compute SHA256 checksum of model file"""
sha256 = hashlib.sha256()
with open(model_path, 'rb') as f:
for chunk in iter(lambda: f.read(4096), b''):
sha256.update(chunk)
return sha256.hexdigest()
def register_model(self, model_path: str, metadata: ModelMetadata):
"""Register new model version"""
# Compute checksum
metadata.checksum = self.compute_checksum(model_path)
# Create unique key
model_key = f"{metadata.name}:{metadata.version}"
# Check if version already exists
if model_key in self.models:
raise ValueError(f"Model {model_key} already registered")
# Register
self.models[model_key] = metadata
self.save_registry()
print(f"Registered model: {model_key}")
return model_key
def get_model(self, name: str, version: Optional[str] = None):
"""Get model metadata"""
if version:
model_key = f"{name}:{version}"
return self.models.get(model_key)
else:
# Get latest version
versions = self.list_versions(name)
if not versions:
return None
latest_version = self.get_latest_version(versions)
model_key = f"{name}:{latest_version}"
return self.models.get(model_key)
def list_models(self):
"""List all registered models"""
models = {}
for key, metadata in self.models.items():
if metadata.name not in models:
models[metadata.name] = []
models[metadata.name].append(metadata.version)
return models
def list_versions(self, name: str):
"""List all versions of a model"""
return [
metadata.version
for key, metadata in self.models.items()
if metadata.name == name
]
def get_latest_version(self, versions: List[str]):
"""Get latest semantic version"""
def parse_version(v):
parts = v.lstrip('v').split('.')
return tuple(int(p) for p in parts)
return sorted(versions, key=parse_version, reverse=True)[0]
def compare_models(self, name: str, version1: str, version2: str):
"""Compare two model versions"""
model1 = self.get_model(name, version1)
model2 = self.get_model(name, version2)
if not model1 or not model2:
return None
comparison = {
'version1': version1,
'version2': version2,
'mAP_diff': model2.mAP - model1.mAP,
'latency_diff': model2.latency_ms - model1.latency_ms,
'size_diff': model2.model_size_mb - model1.model_size_mb
}
return comparison
def search_by_tag(self, tag: str):
"""Search models by tag"""
results = []
for key, metadata in self.models.items():
if tag in metadata.tags:
results.append((key, metadata))
return results
# Example usage
if __name__ == '__main__':
registry = ModelRegistry()
# Register models
models = [
ModelMetadata(
name='yolov8n',
version='v1.0.0',
architecture='YOLOv8',
framework='TensorRT',
precision='INT8',
input_shape=(1, 3, 640, 640),
mAP=37.3,
latency_ms=18.5,
model_size_mb=6.2,
training_date='2025-01-10',
checksum='',
tags=['production', 'jetson-orin'],
description='Initial production model'
),
ModelMetadata(
name='yolov8n',
version='v1.1.0',
architecture='YOLOv8',
framework='TensorRT',
precision='INT8',
input_shape=(1, 3, 640, 640),
mAP=38.1,
latency_ms=19.2,
model_size_mb=6.2,
training_date='2025-01-15',
checksum='',
tags=['production', 'jetson-orin', 'improved-accuracy'],
description='Retrained with additional data'
)
]
for metadata in models:
# Assuming model files exist
model_path = f"{metadata.name}_{metadata.version}.engine"
if not os.path.exists(model_path):
# Create dummy file for example
with open(model_path, 'wb') as f:
f.write(b'dummy_model_data')
registry.register_model(model_path, metadata)
# Query registry
print("\nRegistered models:")
for name, versions in registry.list_models().items():
print(f"{name}: {versions}")
# Get latest
latest = registry.get_model('yolov8n')
print(f"\nLatest yolov8n: {latest.version} (mAP: {latest.mAP})")
# Compare versions
comparison = registry.compare_models('yolov8n', 'v1.0.0', 'v1.1.0')
print(f"\nComparison v1.0.0 vs v1.1.0:")
print(f" mAP improvement: {comparison['mAP_diff']:.2f}")
print(f" Latency change: {comparison['latency_diff']:.2f}ms")
Canary Deployment and Rollback
Safe model deployment requires gradual rollout with automated rollback on performance degradation.
Canary Deployment Manager (Python):
#!/usr/bin/env python3
"""
Canary deployment for safe model rollouts
"""
import time
import random
from dataclasses import dataclass
from typing import Optional
from collections import deque
import numpy as np
@dataclass
class CanaryConfig:
initial_traffic_percent: float = 5.0
increment_percent: float = 10.0
increment_interval_seconds: float = 300 # 5 minutes
max_error_rate: float = 0.02 # 2%
max_latency_increase: float = 0.15 # 15%
min_samples: int = 100
class CanaryDeploymentManager:
"""
Manage canary deployments with automatic rollback
"""
def __init__(self, config: CanaryConfig):
self.config = config
self.current_traffic_percent = 0.0
self.deployment_active = False
# Model versions
self.stable_model = None
self.canary_model = None
# Metrics collection
self.stable_metrics = deque(maxlen=1000)
self.canary_metrics = deque(maxlen=1000)
# Deployment tracking
self.deployment_start = None
self.last_increment = None
self.rollback_triggered = False
def start_deployment(self, stable_version, canary_version):
"""Start canary deployment"""
print(f"\n{'='*60}")
print(f"Starting canary deployment")
print(f"Stable: {stable_version}")
print(f"Canary: {canary_version}")
print(f"Initial traffic: {self.config.initial_traffic_percent}%")
print(f"{'='*60}\n")
self.stable_model = stable_version
self.canary_model = canary_version
self.current_traffic_percent = self.config.initial_traffic_percent
self.deployment_active = True
self.deployment_start = time.time()
self.last_increment = time.time()
self.rollback_triggered = False
def route_request(self):
"""Route request to stable or canary model"""
if not self.deployment_active:
return 'stable'
# Random selection based on traffic percentage
if random.random() * 100 < self.current_traffic_percent:
return 'canary'
else:
return 'stable'
def record_inference(self, model_type, latency, success):
"""Record inference metrics"""
metric = {
'latency': latency,
'success': success,
'timestamp': time.time()
}
if model_type == 'stable':
self.stable_metrics.append(metric)
elif model_type == 'canary':
self.canary_metrics.append(metric)
def evaluate_canary_health(self):
"""Evaluate canary model health"""
if len(self.canary_metrics) < self.config.min_samples:
return True, "Insufficient samples"
# Get recent metrics
recent_stable = list(self.stable_metrics)[-self.config.min_samples:]
recent_canary = list(self.canary_metrics)[-self.config.min_samples:]
if not recent_stable or not recent_canary:
return True, "Insufficient metrics"
# Calculate error rates
stable_errors = sum(1 for m in recent_stable if not m['success'])
canary_errors = sum(1 for m in recent_canary if not m['success'])
stable_error_rate = stable_errors / len(recent_stable)
canary_error_rate = canary_errors / len(recent_canary)
# Calculate latencies
stable_latencies = [m['latency'] for m in recent_stable if m['success']]
canary_latencies = [m['latency'] for m in recent_canary if m['success']]
if not stable_latencies or not canary_latencies:
return True, "No successful inferences"
stable_p95 = np.percentile(stable_latencies, 95)
canary_p95 = np.percentile(canary_latencies, 95)
# Check error rate
if canary_error_rate > self.config.max_error_rate:
return False, f"Error rate too high: {canary_error_rate*100:.2f}%"
# Check latency regression
latency_increase = (canary_p95 - stable_p95) / stable_p95
if latency_increase > self.config.max_latency_increase:
return False, f"Latency regression: {latency_increase*100:.1f}%"
return True, f"Healthy (errors: {canary_error_rate*100:.2f}%, " \
f"latency: {latency_increase*100:.1f}%)"
def update_deployment(self):
"""Update deployment progress"""
if not self.deployment_active or self.rollback_triggered:
return
# Check if time for next increment
if time.time() - self.last_increment < self.config.increment_interval_seconds:
return
# Evaluate canary health
healthy, message = self.evaluate_canary_health()
if not healthy:
print(f"\n⚠ ROLLBACK TRIGGERED: {message}")
self.rollback()
return
# Increment traffic if healthy
if self.current_traffic_percent < 100.0:
self.current_traffic_percent = min(
100.0,
self.current_traffic_percent + self.config.increment_percent
)
self.last_increment = time.time()
print(f"\n✓ Canary healthy: {message}")
print(f" Incrementing traffic to {self.current_traffic_percent}%")
# Complete deployment at 100%
if self.current_traffic_percent >= 100.0:
self.complete_deployment()
def rollback(self):
"""Rollback to stable version"""
self.rollback_triggered = True
self.current_traffic_percent = 0.0
print(f"Rolling back to stable version: {self.stable_model}")
print("Canary deployment aborted")
self.deployment_active = False
def complete_deployment(self):
"""Complete canary deployment"""
duration = time.time() - self.deployment_start
print(f"\n{'='*60}")
print(f"Canary deployment COMPLETED")
print(f"Canary version promoted to stable: {self.canary_model}")
print(f"Deployment duration: {duration/60:.1f} minutes")
print(f"{'='*60}\n")
self.stable_model = self.canary_model
self.canary_model = None
self.deployment_active = False
def get_status(self):
"""Get deployment status"""
if not self.deployment_active:
return {
'active': False,
'stable_model': self.stable_model
}
healthy, message = self.evaluate_canary_health()
return {
'active': True,
'stable_model': self.stable_model,
'canary_model': self.canary_model,
'traffic_percent': self.current_traffic_percent,
'canary_healthy': healthy,
'health_message': message,
'rollback_triggered': self.rollback_triggered
}
# Example usage
if __name__ == '__main__':
config = CanaryConfig(
initial_traffic_percent=5.0,
increment_percent=10.0,
increment_interval_seconds=10, # 10 seconds for demo
max_error_rate=0.02,
max_latency_increase=0.15,
min_samples=50
)
manager = CanaryDeploymentManager(config)
manager.start_deployment('yolov8n-v1.0.0', 'yolov8n-v1.1.0')
# Simulate production traffic
for i in range(500):
model_type = manager.route_request()
# Simulate inference
if model_type == 'stable':
latency = np.random.normal(0.020, 0.003) # 20ms ± 3ms
success = random.random() > 0.005 # 0.5% error rate
else:
# Canary slightly better initially
if i < 200:
latency = np.random.normal(0.019, 0.003) # 19ms ± 3ms
success = random.random() > 0.004 # 0.4% error rate
else:
# Introduce regression for demo
latency = np.random.normal(0.035, 0.005) # 35ms (regression!)
success = random.random() > 0.025 # 2.5% error rate (high!)
manager.record_inference(model_type, latency, success)
# Update deployment
manager.update_deployment()
# Print status periodically
if i % 100 == 0:
status = manager.get_status()
print(f"\nIteration {i}: Traffic to canary: {status['traffic_percent']:.1f}%")
time.sleep(0.02)
# Final status
final_status = manager.get_status()
print(f"\nFinal status: {final_status}")
OTA Updates and Device Fleet Management
Managing 100+ distributed edge devices requires automated OTA update mechanisms with coordinated rollout strategies.
Fleet Management System (Python):
#!/usr/bin/env python3
"""
Fleet management for distributed edge devices
"""
import requests
import hashlib
import os
import time
from dataclasses import dataclass
from typing import List, Dict
from enum import Enum
class DeviceStatus(Enum):
ONLINE = "online"
OFFLINE = "offline"
UPDATING = "updating"
ERROR = "error"
@dataclass
class Device:
device_id: str
ip_address: str
current_version: str
target_version: str
status: DeviceStatus
last_seen: float
update_progress: float = 0.0
class FleetManager:
"""
Manage fleet of edge devices with OTA updates
"""
def __init__(self, update_server_url):
self.update_server_url = update_server_url
self.devices: Dict[str, Device] = {}
def register_device(self, device_id, ip_address, current_version):
"""Register device with fleet"""
device = Device(
device_id=device_id,
ip_address=ip_address,
current_version=current_version,
target_version=current_version,
status=DeviceStatus.ONLINE,
last_seen=time.time()
)
self.devices[device_id] = device
print(f"Registered device: {device_id} ({current_version})")
def plan_rollout(self, target_version, strategy='rolling', batch_size=10):
"""Plan update rollout strategy"""
devices_to_update = [
d for d in self.devices.values()
if d.current_version != target_version
]
print(f"\nPlanning rollout to {target_version}")
print(f"Devices to update: {len(devices_to_update)}")
print(f"Strategy: {strategy}")
if strategy == 'rolling':
# Update in batches
batches = [
devices_to_update[i:i+batch_size]
for i in range(0, len(devices_to_update), batch_size)
]
print(f"Batches: {len(batches)}")
return batches
elif strategy == 'canary':
# First 5% as canary
canary_size = max(1, len(devices_to_update) // 20)
canary = devices_to_update[:canary_size]
remaining = devices_to_update[canary_size:]
print(f"Canary devices: {len(canary)}")
print(f"Remaining: {len(remaining)}")
return [canary] + [
remaining[i:i+batch_size]
for i in range(0, len(remaining), batch_size)
]
else:
# All at once
return [devices_to_update]
def update_device(self, device: Device, target_version, model_url):
"""Trigger OTA update on device"""
print(f"Updating {device.device_id} to {target_version}...")
device.status = DeviceStatus.UPDATING
device.target_version = target_version
try:
# Send update command to device
response = requests.post(
f"http://{device.ip_address}:8080/update",
json={
'version': target_version,
'model_url': model_url,
'checksum': self.compute_checksum(model_url)
},
timeout=5.0
)
if response.status_code == 200:
print(f" ✓ Update initiated on {device.device_id}")
return True
else:
print(f" ✗ Update failed on {device.device_id}: {response.status_code}")
device.status = DeviceStatus.ERROR
return False
except Exception as e:
print(f" ✗ Update failed on {device.device_id}: {e}")
device.status = DeviceStatus.ERROR
return False
def compute_checksum(self, url):
"""Compute checksum of model file"""
# Placeholder - would download and compute actual checksum
return "dummy_checksum"
def execute_rollout(self, target_version, model_url, strategy='rolling'):
"""Execute update rollout"""
batches = self.plan_rollout(target_version, strategy=strategy, batch_size=10)
for batch_idx, batch in enumerate(batches):
print(f"\nBatch {batch_idx + 1}/{len(batches)}")
# Update all devices in batch
for device in batch:
self.update_device(device, target_version, model_url)
# Wait for batch to complete
self.wait_for_batch_completion(batch)
# Verify batch health
if not self.verify_batch_health(batch):
print(f"\n⚠ Batch {batch_idx + 1} failed health check!")
print("Aborting rollout")
return False
print(f"✓ Batch {batch_idx + 1} completed successfully")
print(f"\n{'='*60}")
print(f"Rollout to {target_version} COMPLETED")
print(f"{'='*60}")
return True
def wait_for_batch_completion(self, batch, timeout=300):
"""Wait for batch updates to complete"""
start = time.time()
while time.time() - start < timeout:
# Check device statuses
statuses = [self.get_device_status(d.device_id) for d in batch]
updating = sum(1 for s in statuses if s == DeviceStatus.UPDATING)
completed = sum(1 for s in statuses if s == DeviceStatus.ONLINE)
errors = sum(1 for s in statuses if s == DeviceStatus.ERROR)
print(f" Progress: {completed}/{len(batch)} completed, "
f"{updating} updating, {errors} errors", end='\r')
if completed + errors == len(batch):
print()
break
time.sleep(5)
def get_device_status(self, device_id):
"""Get device status (would query actual device)"""
device = self.devices[device_id]
# Simulate status update
if device.status == DeviceStatus.UPDATING:
# Simulate completion
if time.time() - device.last_seen > 10:
device.status = DeviceStatus.ONLINE
device.current_version = device.target_version
return device.status
def verify_batch_health(self, batch):
"""Verify health of updated batch"""
errors = sum(
1 for d in batch
if self.devices[d.device_id].status == DeviceStatus.ERROR
)
error_rate = errors / len(batch)
return error_rate < 0.05 # Allow 5% error rate
def get_fleet_status(self):
"""Get overall fleet status"""
version_counts = {}
status_counts = {status: 0 for status in DeviceStatus}
for device in self.devices.values():
# Count versions
if device.current_version not in version_counts:
version_counts[device.current_version] = 0
version_counts[device.current_version] += 1
# Count statuses
status_counts[device.status] += 1
return {
'total_devices': len(self.devices),
'versions': version_counts,
'statuses': {
status.value: count
for status, count in status_counts.items()
}
}
# Example usage
if __name__ == '__main__':
manager = FleetManager(update_server_url='https://updates.example.com')
# Register devices
for i in range(50):
manager.register_device(
device_id=f"jetson-{i:03d}",
ip_address=f"192.168.1.{i+100}",
current_version='v1.0.0'
)
# Execute rollout
manager.execute_rollout(
target_version='v1.1.0',
model_url='https://models.example.com/yolov8n-v1.1.0.engine',
strategy='rolling'
)
# Fleet status
status = manager.get_fleet_status()
print(f"\nFleet Status:")
print(f"Total devices: {status['total_devices']}")
print(f"Versions: {status['versions']}")
print(f"Statuses: {status['statuses']}")
Key Takeaways
Production edge AI deployments demand comprehensive operational infrastructure beyond initial model deployment. Distributed monitoring through Prometheus and Jaeger provides essential visibility into fleet-wide performance, enabling proactive issue detection and rapid troubleshooting. Standardized metrics collection across devices enables aggregated analysis identifying systemic issues versus device-specific anomalies.
Data drift detection identifies model degradation requiring retraining before accuracy impacts become severe. Statistical monitoring of input distributions using KS tests and Wasserstein distance provides quantitative drift assessment. Establishing baselines from training data enables ongoing comparison detecting shifts of 10-15% triggering retraining workflows.
Model versioning with semantic versioning and centralized registry enables consistent deployment tracking across distributed fleets. Metadata preservation including accuracy metrics, latency characteristics, and training provenance supports informed rollback decisions. Canary deployments with automated health monitoring and rollback capabilities enable safe model updates with minimal risk. Gradual traffic shifting from 5% through 100% with continuous health evaluation prevents widespread failures from problematic models.
OTA update mechanisms with coordinated rollout strategies manage 100+ device fleets efficiently. Batch-based rolling updates limit blast radius while maintaining overall system availability. Coordinated update orchestration with health verification at each batch prevents cascade failures ensuring fleet stability throughout deployment windows.
This six-part series covered complete production edge CNN deployment from foundational architecture through operational excellence. Combining optimized model quantization, efficient inference servers, advanced resource management, and comprehensive operational tooling enables reliable, performant, scalable edge AI systems meeting real-world deployment requirements. Production maturity requires attention to entire lifecycle: training, optimization, deployment, monitoring, maintenance, and continuous improvement based on production feedback.
References
- Prometheus Monitoring Documentation (https://prometheus.io/docs/introduction/overview/)
- Jaeger Distributed Tracing (https://www.jaegertracing.io/docs/1.40/)
- Grafana Visualization Platform (https://grafana.com/docs/grafana/latest/)
- Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift (https://arxiv.org/abs/2004.03045)
- Martin Fowler: Canary Release Pattern (https://martinfowler.com/bliki/CanaryRelease.html)
- Kubernetes Deployment Strategies (https://kubernetes.io/docs/concepts/workloads/controllers/deployment/)
- Semantic Versioning Specification (https://semver.org/)
- Serving DNNs in Production at Scale (https://www.usenix.org/conference/osdi20/presentation/crankshaw)
