Two-Phase Predictions
Splits inference into fast local model and heavier cloud model for efficiency
Intent & Description
🎯 Intent
Handle resource constraints or latency requirements by splitting inference into two stages: a small, fast model handles common cases locally, escalating to a larger model only when needed.
📋 Context
The best-performing model is often large and compute-hungry, but use cases need predictions under real resource constraints—offline or on-device—or need very low latency for common cases without paying full model cost on every request.
💡 Solution
Split inference into two stages: a small, fast, resource-light model handles common case locally or as trigger, escalating to larger, more capable model (often cloud-hosted) only when situation calls for it. Trades small accuracy on easy cases for responsiveness while reserving full power for hard cases.
Real-world Use Case
- Voice assistants (local wake-word, cloud recognition)
- Offline-capable mobile applications
- Cost-sensitive high-volume systems
- Edge computing with resource constraints
Source
📌 TL;DR
Use a lightweight first-phase model for common cases and escalate to a heavier second-phase model only when needed, balancing responsiveness, cost, and accuracy
Advantages
- Responsive performance for common cases
- Offline capability when needed
- Cost-effective by not running large model on every request
- Enables edge deployment with cloud fallback
Disadvantages
- Two models to maintain and version together
- Added system complexity
- Risk of first phase silently under-triggering
- Requires careful trigger threshold tuning