ML System Design Lifecycle - Roadmap

🔄 ML System Design Lifecycle — End-to-End Overview

1.1: Understand the Lifecycle Stages (The Big Picture)

1. Grasp the canonical ML lifecycle loop: Problem Definition → Data → Features → Training → Evaluation → Deployment → Monitoring → Feedback → Re-training Think of it as a living system, not a linear pipeline.
2. Study key differences from traditional software lifecycles: ML systems degrade (data drift, concept drift), need feedback loops, and rely on probabilistic behavior.
3. Visualize the loop as a continuous control system — inputs (data), outputs (predictions), and feedback (monitoring + retraining).

Deeper Insight: Interviewers often ask, “How would you ensure your ML model continues to perform well after deployment?” The best answers mention monitoring for data drift, automated retraining, and canary rollouts — not just retraining on a schedule.

1.2: Define the Problem Precisely

1. Convert a vague business problem into a measurable ML objective (e.g., “improve engagement” → “predict click-through rate”).
2. Distinguish between prediction, ranking, classification, and forecasting tasks.
3. Define success metrics (business vs. model metrics) — AUC, Precision@K, Recall, RMSE, etc.

Probing Question: “What’s the difference between optimizing F1-score and optimizing user retention?” This checks your ability to tie model metrics → user impact → system metrics.

1.3: Data Strategy & Infrastructure

1. Identify data sources (event logs, databases, third-party APIs) and how they fit into your system.
2. Understand data lineage, data freshness, and the trade-offs between batch vs. streaming pipelines.
3. Learn about feature stores, data validation (e.g., Great Expectations, TFX Data Validation), and schema evolution.

Deeper Insight: A common follow-up: “How would you prevent training-serving skew?” Correct answer involves consistent feature pipelines and centralized feature stores (like Feast).

1.4: Feature Engineering and Management

1. Study feature transformation techniques: normalization, encoding, embeddings.
2. Build reusable, versioned features with metadata (owner, lineage, freshness).
3. Learn feature dependency management and real-time feature computation for low-latency inference.

Probing Question: “If a feature has delayed availability, how do you handle it in production?” Candidates who discuss lag compensation, backfilling, or feature time travel show system maturity.

1.5: Model Training & Experimentation

1. Understand the core training loop and how it integrates with distributed infrastructure (Kubernetes, Ray, or Vertex AI).
2. Learn about experiment tracking (MLflow, Weights & Biases) and model versioning.
3. Implement reproducibility: same data, same code, same environment → same results.

Deeper Insight: “How do you ensure two data scientists running the same experiment get the same results?” Mention version-controlled pipelines, Docker environments, and deterministic seeds.

1.6: Evaluation & Validation

1. Separate data into train / validation / test / shadow production sets properly.
2. Master offline metrics (accuracy, AUC) vs. online metrics (CTR uplift, conversion gain).
3. Simulate live conditions — latency, request load, missing data — before deploying.

Probing Question: “Your model shows high AUC offline but low performance in production. Why?” Expected reasoning: dataset shift, logging bias, feature leakage, or evaluation mismatch.

1.7: Deployment & Serving Infrastructure

1. Learn deployment patterns:

   • Batch prediction systems (e.g., recommendation refresh nightly).
   • Online serving (real-time APIs).
   • Hybrid systems (cached + online ranking).

2. Understand containerization (Docker), inference frameworks (TorchServe, TF Serving, BentoML).

3. Manage rollback and shadow deployments.

Deeper Insight: “What’s the trade-off between deploying via an API vs. embedding the model client-side?” Hint: latency vs. privacy vs. update control.

1.8: Monitoring & Feedback Loops

1. Track both system metrics (latency, throughput) and data/model metrics (drift, confidence, calibration).
2. Implement alerting pipelines for performance degradation.
3. Feed logged predictions + outcomes back into the training store.

Probing Question: “How do you detect data drift in production?” Mention KL divergence, PSI (Population Stability Index), or embedding similarity monitoring.

1.9: Continuous Learning & Automation

1. Learn automated retraining pipelines (Airflow, Kubeflow, TFX).
2. Implement CI/CD for ML: unit tests for data, model validation gates, rollback triggers.
3. Design for human-in-the-loop retraining and feedback incorporation.

Deeper Insight: “When should you not automate retraining?” Great answers discuss risk control — retraining only when drift + performance degradation are statistically significant.

1.10: Scalability, Cost, and Reliability Trade-offs

1. Learn scaling strategies: horizontal scaling of inference services, caching hot predictions, batching requests.
2. Balance latency vs. accuracy: e.g., approximate models for real-time constraints.
3. Monitor cost efficiency: GPU utilization, autoscaling thresholds, cloud costs.

Probing Question: “Your model’s latency doubles after a 10× increase in traffic — what’s your diagnosis path?” Expected reasoning: input queue saturation → thread pool limits → GPU batching configuration.

1.11: Ethical, Privacy, and Regulatory Considerations

1. Know the principles: fairness, interpretability, explainability, and data privacy.
2. Use model cards and dataset documentation for transparency.
3. Be ready to discuss differential privacy, PII handling, and regulatory constraints (GDPR, AI Act).

Deeper Insight: “How do you ensure user data is not leaked through model outputs?” Mention differential privacy, membership inference testing, and audit logs.