Production Model Serving Pipeline: From Training to Inference at Scale

Model Serving Pipeline: The automated workflow from trained model to production endpoint. It includes validation gates, packaging steps, deployment stages, and rollback mechanisms. A mature pipeline deploys models without human intervention while catching failures before they reach users.

Pipeline Stages

Export and validate: Convert trained model to serving format (SavedModel, ONNX). Run validation: compare outputs against golden test set, verify input/output shapes, check for numerical issues (NaN, overflow). Package: Build Docker image with model and dependencies. Tag with version, commit hash, training metadata. Test: Deploy to staging, run integration tests (latency, throughput, correctness). Deploy: Canary to production (1% traffic), monitor metrics, gradually increase if healthy. Rollback: Automated revert if error rate or latency exceeds thresholds.

Validation Gates

Each stage has quality gates that must pass before proceeding. Model validation: AUC above threshold, no prediction drift from baseline, reasonable output distribution. Container validation: image builds successfully, health check passes, inference latency under SLA. Integration validation: end-to-end prediction matches expected format, dependent services receive valid responses. A single failed gate halts the pipeline and alerts the team. False positives are costly (blocked deployments), but false negatives are worse (broken models in production).

Deployment Strategies

Blue-green: Run old and new versions in parallel, switch traffic atomically. Fast rollback but requires 2x resources during transition. Canary: Route small percentage of traffic to new version, gradually increase. Catches problems early but takes longer to fully deploy. Shadow: New version receives traffic copy but responses are discarded. Validates performance without user impact but does not test real correctness.

Automation Goal: A new model should deploy to production within hours of training completion, with zero manual steps and automatic rollback on failure.