Model Deployment and Serving

Deploying models safely and reliably requires engineering beyond training. This post covers patterns for serving models at scale.

Packaging

• Containerize models with minimal runtime (e.g., FastAPI, Flask, or a model server).
• Pin model versions and dependencies; store artifacts in an immutable registry.

Serving patterns

• Batch inference: Good for large-scale offline predictions.
• Online inference (low-latency): Use optimized runtimes, quantized models, and autoscaling.
• Streaming/edge: Deploy lightweight models near users for low-latency scenarios.

Reliability and rollout

• Use canary or blue/green deployments for safe rollouts.
• Feature flags to toggle new models or behaviors.
• Circuit breakers and graceful degradation when model endpoints fail.

Monitoring and observability

• Collect latency, error rates, and request volumes.
• Monitor model quality drift: compare predictions to later-collected ground truth.
• Log inputs/outputs for debugging, respecting privacy and sampling.

Security and cost

• Rate limit public endpoints and authenticate requests.
• Use batching and caching for cost efficiency when possible.

Next steps: add automated smoke tests and a rollback plan before production launches.