Execution Context
Shadow deployment enables ML engineers to validate new models against live production data without disrupting end-user services. By routing a small percentage of traffic to the new model while keeping the original model active, organizations can assess latency, accuracy, and cost implications in real time. This approach minimizes risk during the transition from testing environments to production, ensuring that performance metrics align with business expectations before committing to full adoption.
The new model runs concurrently with the existing production model but does not influence user-facing responses.
Traffic is routed to both models simultaneously, allowing direct comparison of inference outputs and performance metrics.
Data from the shadow run is logged for analysis without being exposed to the end-user interface.
Operating Checklist
Define traffic split percentage (e.g., 10%) for the new model in the serving configuration.
Enable shadow mode in the deployment pipeline to ensure silent inference execution.
Activate concurrent routing so both models process requests simultaneously.
Monitor key performance indicators and compare outputs against baseline metrics.
Integration Surfaces
Model Serving Gateway
Configures dual routing rules to split traffic between the legacy and new model endpoints.
Monitoring Dashboard
Displays real-time latency, throughput, and error rates for both active and shadow models.
Data Lake
Stores anonymized inference logs from the shadow run for post-deployment analysis and drift detection.
