ML System Design — Monitoring & Observability

1.1: Understand the Role of Monitoring in the ML System Lifecycle

• Learn the full loop: Training → Deployment → Monitoring → Retraining.
• Understand how model monitoring differs from traditional software observability (e.g., logs and uptime are not enough; we track prediction quality and data integrity).
• Be ready to explain how monitoring connects to data pipelines, feature stores, retraining triggers, and alerting systems.

Deeper Insight: A common interview follow-up: “If the model’s accuracy drops by 5%, how do you debug whether it’s due to data drift, concept drift, or label errors?” Your answer should show a structured root-cause analysis mindset.

1.2: Data Drift

Learning Steps:

1. Define data drift as a change in the feature distribution between training and production data.
2. Study distribution comparison metrics:
   • Kolmogorov–Smirnov (KS) Test for continuous features.
   • Population Stability Index (PSI).
   • Jensen–Shannon Divergence and Earth Mover’s Distance.
3. Implement data drift detection with Evidently, WhyLabs, or custom Python code using statistical tests.
4. Learn how to visualize drift using histograms, cumulative distribution plots, or PSI dashboards.

Probing Question: “How would you detect drift in categorical vs. continuous features?” Be ready to discuss chi-square tests, mutual information, and sampling frequency trade-offs.

1.3: Concept Drift

Learning Steps:

1. Define concept drift as the change in the conditional distribution \( P(y|X) \).
2. Learn to detect drift using:
   • Performance metrics over time (e.g., AUC, F1-score trendlines).
   • Sliding window evaluation.
   • Drift detectors like DDM, EDDM, or ADWIN.
3. Explore retraining strategies:
   • Periodic retraining.
   • Online learning.
   • Active drift-triggered retraining.
4. Study case studies: recommendation systems (seasonal shifts), fraud detection (evolving adversaries).

Probing Question: “Your model’s precision remains high, but recall drops sharply. Is this data drift or concept drift?” Be ready to reason that the underlying concept likely changed, altering the positive class boundary.

1.4: Model Performance Monitoring

Learning Steps:

1. Track predictive metrics: accuracy, precision, recall, F1, ROC AUC — per time window.
2. Track business KPIs linked to model outcomes (conversion rate, false positive cost).
3. Implement confidence-based monitoring: monitor prediction entropy or calibration drift.
4. Set up automated alerts when metrics cross thresholds (e.g., via Prometheus, Grafana, or Datadog).

Deeper Insight: “How would you handle label latency in monitoring?” — explain shadow evaluation, delayed feedback handling, and proxy metric tracking (like model uncertainty trends).

1.5: Data Quality and Integrity Checks

Learning Steps:

1. Learn to apply data validation frameworks (e.g., Great Expectations, TFDV).
2. Understand common data quality metrics: missing rates, outlier ratios, schema deviations.
3. Implement automated validation at ingestion and before training.
4. Integrate checks into CI/CD pipelines and block bad data upstream.

Probing Question: “What if your model accuracy drops but data drift metrics are stable?” — you might have hidden quality issues or label contamination.

1.6: Model Explainability in Monitoring

Learning Steps:

1. Use SHAP, LIME, or Integrated Gradients to track feature importance drift over time.
2. Build explainability dashboards correlating changes in feature influence with performance metrics.
3. Detect bias drift — when model fairness metrics degrade for certain demographic segments.

Probing Question: “If SHAP feature importances suddenly shift, what might it indicate?” — possible data schema changes, retraining artifacts, or population shifts.

1.7: Monitoring Infrastructure and Architecture

Learning Steps:

1. Learn the architectural flow:
   • Inference logging → Data aggregation → Metric computation → Alerting → Feedback loop.
2. Design feature logging strategies (balance completeness vs. cost).
3. Use distributed monitoring tools (Prometheus, Grafana, ELK, or OpenTelemetry).
4. Integrate with MLOps orchestration (Kubeflow, MLflow, Vertex AI, or SageMaker Model Monitor).

Probing Question: “What’s the cost of over-logging?” — be ready to discuss storage overhead, PII risks, and sampling policies.

1.8: Continuous Evaluation & Retraining Pipelines

Learning Steps:

1. Automate performance reports and retraining triggers.
2. Implement canary evaluations before rolling out new models.
3. Build pipelines for model version comparison (champion–challenger setup).
4. Study real-world drift feedback mechanisms in streaming and batch inference setups.

Probing Question: “How do you decide when to retrain?” — discuss thresholds, data freshness, and business-critical events.

1.9: Alerting, Logging, and Anomaly Detection

Learning Steps:

1. Build metric-based alerting pipelines (e.g., “alert if PSI > 0.2 or accuracy drops > 10%”).
2. Implement time-series anomaly detection using EWMA or Prophet.
3. Define severity levels and escalation workflows.
4. Balance sensitivity and stability — tune alert thresholds to avoid alert fatigue.

Deeper Insight: “Why do most ML monitoring systems fail in production?” — poorly calibrated alerts, lack of ownership, and missing feedback loops.

1.10: End-to-End System Design Synthesis

Learning Steps:

1. Design an end-to-end Monitoring System for a Recommendation Model:
   • What metrics would you track?
   • How would you log and visualize them?
   • How would retraining be triggered?
   • How would you detect silent failures?
2. Prepare to whiteboard a monitoring architecture diagram showing:
   • Model registry
   • Feature store
   • Monitoring DB
   • Alerting engine
   • Retraining workflow

Probing Question: “If your model fails silently for 3 days, what do you do?” — this tests your ability to combine diagnosis, mitigation, and process accountability.