Content Moderation Pipeline — Simple

Problem statement (interviewer prompt)

Design a content-moderation pipeline for user-generated images + videos + text. Auto-classify with ML (NSFW, violence, hate speech, CSAM), route ambiguous cases to human reviewers, support appeals + audit trails, and meet regulatory deadlines (e.g. 24h take-downs).

flowchart LR
  UP[Upload / Post]
  ML([ML classifiers])
  RULES[Policy rules]
  Q[[Review queue]]
  HUM[Human moderators]
  ACT[Action]
  UP --> ML --> RULES --> ACT
  RULES --> Q --> HUM --> ACT

    classDef client fill:#dbeafe,stroke:#1e40af,stroke-width:1px,color:#0f172a;
    classDef edge fill:#cffafe,stroke:#0e7490,stroke-width:1px,color:#0f172a;
    classDef service fill:#fef3c7,stroke:#92400e,stroke-width:1px,color:#0f172a;
    classDef datastore fill:#fee2e2,stroke:#991b1b,stroke-width:1px,color:#0f172a;
    classDef cache fill:#fed7aa,stroke:#9a3412,stroke-width:1px,color:#0f172a;
    classDef queue fill:#ede9fe,stroke:#5b21b6,stroke-width:1px,color:#0f172a;
    classDef compute fill:#d1fae5,stroke:#065f46,stroke-width:1px,color:#0f172a;
    classDef storage fill:#e5e7eb,stroke:#374151,stroke-width:1px,color:#0f172a;
    classDef external fill:#fce7f3,stroke:#9d174d,stroke-width:1px,color:#0f172a;
    classDef obs fill:#f3e8ff,stroke:#6b21a8,stroke-width:1px,color:#0f172a;
    class UP,RULES,HUM,ACT service;
    class Q queue;
    class ML compute;