ML System Architecture Fundamentals - Roadma

🏗️ ML System Architecture Fundamentals

1.1: Understand End-to-End ML System Anatomy

• Learn the 5 core components of every ML system:

  1. Data Pipeline — Ingestion, cleaning, feature extraction
  2. Model Training Pipeline — Experimentation and retraining
  3. Model Registry — Versioning, validation, approval
  4. Model Serving Layer — Real-time or batch inference
  5. Monitoring & Feedback Loop — Drift, quality, and performance metrics

• Study canonical architectures for fraud detection, recommendation, and ranking systems.

Deeper Insight: Be ready to whiteboard how features move from raw data → features → model → predictions → feedback. Interviewers often probe: “Where would you put feature engineering logic — in training, serving, or both?” The right answer emphasizes feature parity between offline and online components.

1.2: Design Principles for Scalable ML Systems

• Master system properties: Scalability, Availability, Consistency, and Latency.
• Learn how data flow differs between online prediction and offline training.
• Understand why ML systems prefer immutable data stores, append-only event logs, and versioned artifacts.
• Study batch, streaming, and hybrid (lambda) architectures and where each fits best.

Deeper Insight: “What happens if feature generation is delayed by 10 minutes?” → This question tests your ability to discuss event-time consistency and serving skew. The best answers reference feature stores or event backfilling strategies.

1.3: Fault Tolerance, Redundancy, and Consistency Models

• Study fault domains — what happens when a model server dies mid-inference or a data pipeline job fails mid-run.
• Learn recovery patterns: checkpointing, retry queues, idempotent writes, and graceful degradation (e.g., fallback models).
• Understand CAP theorem trade-offs in ML contexts (e.g., why online systems may favor availability over consistency).

Probing Question: “Suppose your model prediction service is down — how do you keep the system functional?” Discuss fallback heuristics, default scores, or last-known-good models to maintain user experience.

1.4: Data and Feature Management Layer

• Master Feature Store concepts — how features are defined, stored, versioned, and served to both training and inference pipelines.
• Learn about offline vs. online stores, TTL policies, materialization, and point-in-time correctness.
• Explore how entity joins, feature freshness, and backfill errors affect model quality.

Probing Question: “How do you ensure the same feature computation during training and inference?” This tests whether you understand feature consistency — often solved by a unified store or feature transformation framework.

1.5: Real-Time vs. Batch System Trade-offs

• Compare online scoring (real-time) vs batch scoring (offline) systems.
• Quantify latency targets — e.g., <100ms for ranking systems, vs. minutes for offline scoring.
• Learn techniques for asynchronous inference, model caching, and pre-computed embeddings to reduce latency.

Deeper Insight: “You’re designing an ad-ranking model that must respond in <50ms — what optimizations would you apply?” Top answers discuss feature prefetching, model quantization, and GPU batching vs CPU parallelism trade-offs.

1.6: Model Versioning and Deployment Architecture

• Study model lifecycle: training → evaluation → registration → shadow testing → A/B rollout → monitoring.

• Learn deployment patterns:

  • Canary Deployments
  • Shadow Mode Inference
  • Blue/Green Model Switching

• Understand feature compatibility and schema versioning across model generations.

Probing Question: “If your new model performs better offline but worse online, what’s your debugging approach?” Discuss data leakage, feedback loop bias, or stale features as likely culprits.

1.7: Monitoring, Drift Detection, and Feedback Loops

• Learn to instrument metrics: data quality, prediction drift, latency, error rate, user engagement.
• Implement population stability index (PSI) and KL divergence for drift detection.
• Understand closed-loop retraining — how fresh labels or user feedback re-enter the system.

Deeper Insight: “How do you detect that your recommendation system is decaying in quality?” The interviewer wants to hear about proxy metrics (CTR, engagement time), statistical drift, and alerting thresholds.

1.8: Multi-Tenancy and Resource Management

• Explore how large organizations serve multiple models per team or product.
• Learn about model serving platforms (TensorFlow Serving, Triton, Ray Serve) that handle routing, scaling, and concurrency.
• Study how autoscaling policies, GPU/CPU allocation, and container orchestration (Kubernetes) ensure reliable multi-tenant serving.

Probing Question: “How would you design a platform that can serve 100 models with different latency SLAs?” Be ready to discuss resource partitioning, load balancing, and dynamic batching.

1.9: Security, Privacy, and Governance

• Understand data encryption, PII redaction, model access control, and audit trails.
• Learn about model inversion and membership inference attacks.
• Study governance frameworks: Model Cards, Lineage Tracking, and Explainability Reports.

Deeper Insight: “What’s the difference between data-level privacy and model-level privacy?” Discuss Differential Privacy, Federated Learning, and Secure Aggregation mechanisms.

1.10: Putting It All Together — Designing End-to-End Systems

• Combine concepts into case studies:

  • Fraud Detection — streaming inference, high recall, event-time joins
  • Recommendation Engine — user/item embeddings, retrieval + ranking stack
  • Ads Ranking System — real-time auction, latency-constrained scoring, multi-objective optimization

• Practice designing with trade-offs:

  • Accuracy vs. Latency
  • Personalization vs. Scalability
  • Freshness vs. Stability

Probing Question: “If you had to re-architect your model to cut latency in half without losing much accuracy — what knobs can you turn?” The best candidates discuss model distillation, feature pruning, caching, and approximation algorithms.