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Observability For Inference Services

Learn Observability For Inference Services for free with explanations, exercises, and a quick test (for Computer Vision Engineer).

Published: January 5, 2026 | Updated: January 5, 2026

Table of Contents

Why this matters

In production vision systems, great models can still fail users if the service is slow, unstable, or drifting. Observability lets you see inside your inference pipeline to catch issues early and fix them fast.

  • Meet latency budgets for live video analytics, mobile AR, or retail checkout.
  • Detect data drift (lighting, camera angle, motion blur) before accuracy tanks.
  • Diagnose tail latency spikes, GPU/CPU saturation, memory leaks, and batching issues.
  • Prove service reliability with SLOs and on-call ready alerts.

Concept explained simply

Monitoring tells you if something is wrong. Observability helps you understand why. You collect signals from each stage of the inference path so you can explain behavior without shipping new code.

  • Metrics: numbers over time (latency, throughput, GPU utilization, error rate).
  • Logs: structured event records with context (request_id, stage, error).
  • Traces: end-to-end timing across stages (preprocess β†’ model β†’ postprocess).
  • Events: deployments, configuration changes, model version switches.

Mental model

Think of your pipeline as an airport security line with scanners:

  • Queue length shows backlog at each checkpoint.
  • Per-stage timers show which checkpoint is slow.
  • IDs travel with each passenger (correlation IDs) to reconstruct their path.
  • Quality checks (image brightness/blur) ensure scanners see clearly.
Typical inference path (click to expand)
  • Client β†’ Gateway β†’ Preprocess (resize, normalize) β†’ Model (GPU) β†’ Postprocess (NMS, tracking) β†’ Writer (DB/queue)
  • Attach a request_id at the gateway and pass it through all stages.
  • Emit a trace span per stage and label metrics by stage, model_version, and camera_id (or data source).

Core signals to track

  • Latency: P50/P95/P99 per stage and end-to-end; queue wait time.
  • Throughput: requests/sec, frames/sec, batch size in use.
  • Errors: HTTP 5xx, model inference errors, timeouts, OOMs, dropped frames.
  • Resources: GPU/CPU utilization, GPU memory, VRAM fragmentation, disk I/O, network.
  • Model outputs: class distribution, mean confidence, bbox count per frame, rejected detections.
  • Data quality: brightness, exposure, blur/sharpness, resolution, aspect ratio, occlusion rate.
  • Drift: embedding distance vs reference, feature histograms shift, label distribution shift (if labels exist).
  • Business-facing: SLA/SLO compliance, cost per 1k inferences, dropped-camera ratio.

SLOs and alerts

  • Availability SLO: e.g., 99.5% of requests return 2xx.
  • Latency SLO: e.g., 95% of inferences finish under 120 ms.
  • Quality guardrail: mean confidence over last 10k frames above 0.6, or embedding drift below threshold.
Alert examples
  • Fast-burn latency: if P95 > SLO target for 5 minutes and burn rate > 2 β†’ page on-call.
  • GPU saturation: GPU util > 90% for 10 minutes β†’ warn; > 95% + latency rising β†’ critical.
  • Drift detection: embedding mean distance > 1.5Γ— baseline for 30 minutes β†’ open ticket with canary plan.

Worked examples

Example 1: Tail latency spike after deploy

Symptoms: P50 stable at 60 ms, P95 jumps from 110 ms to 300 ms, GPU util unchanged.

  • Traces show longer queue wait time at preprocess, not GPU.
  • Batch size label shows more small batches after deploy (auto-batcher bug).
  • Fix: restore batcher threshold, add alert on queue_wait_ms and small-batch rate.
Example 2: Model drift during rainy evenings

Symptoms: mean confidence drops 15% 18:00–22:00, false positives rise. Data quality shows lower brightness and more motion blur.

  • Embedding drift vs dry-day reference increases by 40% in that window.
  • Fix: add low-light enhancement in preprocess, schedule training with rainy-evening samples, introduce time-aware canary.
Example 3: Memory leak in postprocess

Symptoms: RSS grows 50 MB/hour, periodic OOMs, error spikes every few hours.

  • Logs show missing tensor.detach() and large arrays kept in cache.
  • Fix: detach tensors, cap cache, add gauge for in-memory queue size and heap usage.
Example 4: Throughput collapse

Symptoms: Throughput halves, latency doubles, GPU util 40%.

  • Traces: network read time up; image size distribution grew due to upstream change.
  • Fix: enforce max resolution in gateway, add alert on input resolution drift.

Instrumentation checklist

  • Correlation request_id added at ingress and logged everywhere
  • Per-stage timers (preprocess/model/postprocess) with histograms
  • Queue length and queue wait time metrics
  • Resource metrics: GPU/CPU util, VRAM, batch size, threads
  • Output distribution: confidences, class histogram, bbox counts
  • Data quality: brightness, blur, resolution, aspect ratio
  • Drift signal: embedding distance vs reference
  • Structured logs with severity and request_id
  • SLOs defined with alert rules and burn rates

How to instrument (step-by-step)

  1. Define an ID strategy: generate request_id at gateway; propagate via headers or context.
  2. Trace the pipeline: start a root span at ingress; child spans per stage with labels stage, model_version, camera_id.
  3. Emit metrics:
    • Counters: requests_total, errors_total.
    • Histograms: latency_ms{stage}, queue_wait_ms.
    • Gauges: gpu_util, gpu_mem_bytes, in_flight_requests.
  4. Log minimally but use structure: JSON lines with request_id, stage, severity, message.
  5. Compute lightweight data quality features per request (brightness/blur) and sample 1–5% of raw frames for audits with privacy safeguards.
  6. Maintain a reference set for drift and recompute embedding baselines periodically.
  7. Create dashboards per SLO: latency percentiles by stage, error rate, drift over time, resource headroom.
  8. Wire alerts: start with warning thresholds; add paging only for user-impacting burn rate breaches.
Privacy and cost safety checks
  • Mask faces/license plates if sampling frames; prefer storing features/embeddings over raw images.
  • Rate-limit sampling; expire samples after a short retention window.
  • Log at INFO/DEBUG only for sampled requests; keep PII out of logs.

Exercises

Do these now. They mirror the graded exercises below and help you pass the quick test.

Exercise 1: Find the bottleneck

Given the following time series snapshot, identify the likely root cause and a first fix.

P50 end_to_end_ms: 65 β†’ 68
P95 end_to_end_ms: 120 β†’ 290
P95 preprocess_ms: 15 β†’ 16
P95 queue_wait_ms: 8 β†’ 140
GPU util: 72% β†’ 72%
Batch size (avg): 8 β†’ 2
Error rate: stable
  • Expected: state the bottleneck and propose one fix and one alert.

Exercise 2: Draft SLOs and alerts

Design SLOs and two alert rules for a city traffic camera detector (30 FPS per stream, real-time). Include thresholds and conditions.

  • Expected: availability SLO, latency SLO, one resource alert, one quality/drift alert.
Hints
  • For Exercise 1, watch queue wait time and batch size.
  • For Exercise 2, define specific percentiles and durations; tie alerts to burn rate or sustained breach.

Common mistakes and self-check

  • Only tracking mean latency. Fix: always collect P50/P95/P99 and per-stage breakdown.
  • No request correlation. Fix: mandatory request_id in logs, metrics labels in traces.
  • High-cardinality labels (e.g., request_id in metrics). Fix: keep metrics low-cardinality; put high-cardinality in logs/traces.
  • Ignoring data quality. Fix: compute brightness/blur/resolution features and watch trends.
  • Alert fatigue. Fix: use burn-rate alerts and group similar alerts; page only on user impact.
  • Storing raw frames indiscriminately. Fix: sample, redact, expire quickly.
Self-check
  • Can you explain the cause of a latency spike using traces and queue metrics?
  • Do you have a drift metric that correlates with accuracy drop?
  • Do your alerts reflect SLOs, not just thresholds?

Practical projects

  • Stage-timed microservice: build a toy image classifier service with timers for each stage and a dashboard showing P50/P95 latency per stage.
  • Drift monitor: compute embeddings on a reference dataset and live data; alert when the mean distance exceeds a threshold for 30 minutes.
  • Alerting ladder: implement warning and paging rules using burn rates for latency SLO; simulate load to test behavior.

Who this is for

  • Computer Vision Engineers shipping or maintaining inference APIs or streaming pipelines.
  • MLOps/Platform engineers integrating vision workloads into production.

Prerequisites

  • Comfort with Python and a deep learning framework (e.g., PyTorch or TensorFlow).
  • Basic understanding of containers and orchestration (e.g., Docker, Kubernetes) is helpful.
  • Familiarity with metrics, logs, and traces concepts.

Learning path

  1. Deployment pipelines for vision models (build, containerize, release).
  2. Observability for inference services (this lesson).
  3. A/B testing and canaries for vision models.
  4. Autoscaling and cost optimization for GPU workloads.

Mini challenge

Write a one-page incident report for a simulated outage where P95 latency doubles but GPU util stays flat. Include:

  • Timeline with key metrics and traces referenced.
  • Root cause hypothesis and validation steps.
  • Immediate mitigation and permanent fix.
  • New metric or alert you will add.

Next steps

  • Automate canary rollouts tied to drift and quality signals.
  • Tune batching and concurrency based on observed tail latency.
  • Track cost per inference and add cost guardrails.

Quick Test

The Quick Test is available to everyone. Log in to save your progress and see your history.

When you are ready, take the Quick Test below.

Practice Exercises

2 exercises to complete

Instructions

Use the provided snapshot to identify the bottleneck and propose one immediate fix and one alert rule.

P50 end_to_end_ms: 65 β†’ 68
P95 end_to_end_ms: 120 β†’ 290
P95 preprocess_ms: 15 β†’ 16
P95 queue_wait_ms: 8 β†’ 140
GPU util: 72% β†’ 72%
Batch size (avg): 8 β†’ 2
Error rate: stable
Expected Output
Root cause points to queue wait time increase due to small batch sizes; propose fixing batcher thresholds and adding alert on queue_wait_ms and small-batch rate.

Observability For Inference Services β€” Quick Test

Test your knowledge with 8 questions. Pass with 70% or higher.

8 questions70% to pass
Monitoring Data Drift And QualityIncident Response For Degradation

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