Production Implementation and Serving Architecture

Production Architecture
Deploying multi-task models requires careful API design. Users may need only one task output, all task outputs, or a subset. The serving infrastructure must handle these patterns efficiently.
Shared Backbone, Selective Heads
The most common pattern runs the shared backbone once, then activates only requested task heads. If a user needs only detection, skip the segmentation head entirely. This saves 10-30% compute per request depending on head complexity.
Implementation: Accept a task mask in the API request. During inference, check the mask and skip heads for unrequested tasks. Cache backbone outputs if the same input needs multiple task outputs sequentially.
Latency Considerations
Multi-task models are larger than single-task models. The shared backbone handles more diverse features, often requiring more parameters. Expect 20-50% higher latency compared to a specialized single-task model.
Trade-off decision: A multi-task model at 80ms versus three single-task models at 30ms each (90ms total for all three). If users typically need all outputs, multi-task wins. If users typically need one output, single-task models win.
Model Updates and Rollback
Updating a multi-task model affects all tasks simultaneously. If a new version improves detection but degrades segmentation, you cannot roll back just segmentation. This coupling increases deployment risk.
Mitigation: Maintain per-task evaluation metrics. Block deployment if any task regresses beyond threshold. Consider hybrid architectures where critical tasks have dedicated fallback models.
⚠️ Key Trade-off: Multi-task models simplify infrastructure (one model to serve) but complicate deployment (all tasks coupled). Weigh operational simplicity against deployment flexibility.

💡 Key Takeaways

✓Selective head execution saves 10-30% compute by skipping unrequested task outputs

✓Multi-task models are 20-50% slower than single-task models due to larger shared backbones

✓If users need all tasks, multi-task wins; if users need one task, single-task models win on latency

✓Coupled deployment: improving one task while regressing another blocks the entire update

📌 Interview Tips

1Interview Tip: Discuss the all-or-nothing rollback problem - multi-task models cannot partially revert

2Interview Tip: Mention selective head execution as an optimization when users only need subset of outputs

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