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ML System Design

Overview

ML System Design is a critical skill for ML engineers, combining machine learning knowledge with systems thinking. It involves designing end-to-end ML systems that are scalable, reliable, and maintainable. This section covers the key components of ML systems and real-world design patterns commonly discussed in interviews.

ML System Design Framework

graph TD
    A[Problem Definition] --> B[Data]
    B --> C[Features]
    C --> D[Model]
    D --> E[Serving]
    E --> F[Monitoring]
    F --> G[Iteration]
    G --> A

The FRAMEWORK

  1. Formulate the problem (metrics, constraints)
  2. Requirements (latency, throughput, scale)
  3. Architecture (data pipeline, feature store, model, serving)
  4. Model selection and training
  5. Evaluation (offline + online metrics)
  6. Workflow (training pipeline, retraining)
  7. Operations (monitoring, alerting, debugging)
  8. Rollout (A/B testing, canary deployment)
  9. Key trade-offs and alternatives

Key System Components

graph TD
    A[ML System] --> B[Data Pipeline]
    A --> C[Feature Store]
    A --> D[Training Pipeline]
    A --> E[Model Serving]
    A --> F[Monitoring]
    B --> B1[Ingestion, validation, transformation]
    C --> C1[Online + Offline features]
    D --> D1[Training, evaluation, registry]
    E --> E1[Real-time, batch, streaming]
    F --> F1[Drift, performance, business metrics]

Interview Questions

  1. How do you approach ML system design questions? — Clarify requirements → Define metrics → Design data pipeline → Choose model → Design serving architecture → Plan monitoring → Discuss trade-offs.

  2. What are the key differences between ML system design and traditional system design? — ML systems need data pipelines, feature stores, model training/retraining, A/B testing, and drift monitoring on top of traditional distributed systems concerns.

  3. How do you handle model updates in production? — CI/CD pipelines with automated training, evaluation gates, canary/blue-green deployment, and rollback capability.

Cross-References