Model Deployment

A/B testing infrastructure for ML models

Compare model performance in production with automated traffic splitting and metrics collection.

What this covers

Set up production A/B testing for ML models with automated traffic splitting, data capture, and statistical analysis to make data-driven deployment decisions.

Implementation trail

Multi-model endpoint configuration
API Gateway traffic splitting
Data capture and monitoring
Statistical analysis setup
Automated decision workflows

Configure multi-model endpoints

Deploy SageMaker endpoints with multiple production variants for A/B testing.
Configure traffic weights between model variants with gradual rollout capabilities.
Enable data capture to record all inputs and outputs for analysis.

Set up API Gateway routing

Create API Gateway endpoints that route traffic to SageMaker with proper authentication.
Implement request/response transformation to add metadata for tracking.
Configure throttling and rate limiting to protect model endpoints.

Implement comprehensive monitoring

Set up CloudWatch metrics for latency, error rates, and throughput per model variant.
Create custom metrics for business KPIs like conversion rates or prediction accuracy.
Configure alarms for significant performance degradation or error spikes.

Analyze test results

Use captured data to perform statistical significance testing between variants.
Monitor key metrics over time to detect performance trends and seasonal effects.
Implement automated decision rules for promoting winning models to full traffic.

Ready to test models in production?

We implement A/B testing infrastructure that lets you safely compare model performance and make data-driven deployment decisions.

Deploy confident model updates