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Modern computer vision (CV) systems need end-to-end observability: real-time resource and latency monitoring, distributed tracing across services, centralized logs, and long-horizon analytics. In this guide, we show how to monitor a CV pipeline with Amazon CloudWatch and AWS X-Ray (for inference latency, GPU/CPU/memory, and request traces), how to store historical metrics in MongoDB Time Series collections, and how to surface insights in a React dashboard. We’ll also compare Prometheus/Grafana and Datadog options and share best practices specific to vision workloads. (MongoDB, Prometheus, Grafana Labs, docs.datadoghq.com)
Effective monitoring starts with the right KPIs for ML on Kubernetes/EC2: resource utilization (CPU, memory, and GPU), inference latency & throughput, model performance (accuracy/precision/recall), data/label drift, and error rates. AWS’s ML observability guidance for EKS highlights these “golden signals” and stresses targeting high GPU utilization to avoid waste and contention. See Intro to observing ML on Amazon EKS and EKS best practices for AI/ML observability. (Amazon Web Services, Inc., AWS Documentation)
GPU monitoring. Install the CloudWatch agent with NVIDIA GPU support to capture GPU utilization, memory, temperature, and power on EC2/EKS nodes. AWS also provides prebuilt solutions and dashboards for NVIDIA workloads and Container Insights guides for GPUs on EKS. (AWS Documentation)
Custom application metrics. Publish inference timings and throughput as custom metrics so you can graph p50/p95/p99 and alert on SLOs. Example (Python): use boto3 PutMetricData to send InferenceLatency with dimensions like model name/version. (Boto3)
Centralized logs. Ship stdout or file logs to CloudWatch Logs (or the Logs agent) for search, retention, and alarms using Logs Insights/metric filters. (AWS Documentation)
Complex CV pipelines span preprocessing → inference → postprocessing → DB writes. AWS X-Ray traces each request end-to-end, with a service map and timeline to pinpoint bottlenecks and failures. See Viewing traces & details and Using the X-Ray trace map. You can also correlate traces with metrics/logs via CloudWatch ServiceLens integration. (AWS Documentation)
Correlate logs ↔ traces. Include the X-Ray trace ID in your structured JSON logs to jump from an alarm to the exact request’s logs and trace. AWS’s observability series shows how to add trace IDs in logs: “.NET observability: logging”. (Amazon Web Services, Inc.)
For month-over-month trends (accuracy, latency creep, error rates), store metrics in MongoDB Time Series collections. Time Series offers columnar storage, automatic time/metadata indexing, and reduced disk usage versus regular collections—ideal for fast aggregations over large telemetry sets. See Benefits and Best practices. (MongoDB)
Expose a simple API (e.g., /metrics) that queries MongoDB for accuracy, drift metrics, latency percentiles, usage, error rates, GPU utilization. In React, render charts (e.g., via Chart.js/Recharts) and add filters (date range, model version, camera/site). This mirrors Grafana-style dashboards but tuned to your CV KPIs; for reference on dashboard patterns, see Grafana’s dashboard docs. (Grafana Labs)
Prometheus + Grafana (open source / managed).
Expose Prometheus metrics (counters, gauges, histograms, summaries) from your services and scrape them with Prometheus; visualize in Grafana. See Prometheus metric types and PromQL basics. AWS offers managed options—Amazon Managed Service for Prometheus and Amazon Managed Grafana—plus EKS integrations. (Prometheus, AWS Documentation)
Datadog (hosted, all-in-one).
Datadog unifies metrics, logs, and APM traces with GPU integrations (NVIDIA DCGM/NVML). See APM/Tracing, DCGM integration, and Logs collection/parsing. It’s a fast path to full-stack observability for CV workloads on EC2/EKS with GPUs. (docs.datadoghq.com)
OpenTelemetry/ADOT (vendor-neutral instrumentation).
Instrument once with OpenTelemetry and route to CloudWatch, X-Ray, Prometheus, or Datadog. On AWS, use AWS Distro for OpenTelemetry (ADOT) and its collectors/operators for EKS to ship metrics/traces to your chosen backend. See ADOT ↔ X-Ray and ADOT collector → AMP. (AWS Distro for OpenTelemetry, OpenTelemetry, AWS Documentation)
Use this on the server side where inference runs.
import time, boto3
cloudwatch = boto3.client('cloudwatch')
start = time.time()
# ... run model inference ...
elapsed_ms = (time.time() - start) * 1000
cloudwatch.put_metric_data(
Namespace='CVPipeline',
MetricData=[{
'MetricName': 'InferenceLatency',
'Dimensions': [
{'Name': 'ModelName', 'Value': 'ResNet50-v2'},
{'Name': 'Stage', 'Value': 'Inference'}
],
'Value': elapsed_ms,
'Unit': 'Milliseconds'
}]
)
(Referenced API: PutMetricData.) (Boto3)
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