model-serving

Design safe ML model serving rollouts by defining serving surfaces, planning canary or shadow deployments, and establishing rollback safety gates to prevent incidents. This ensures controlled feature contract alignment across model versions.

Define online inference contracts by specifying clear request and response schemas for your serving surfaces. This establishes explicit feature contracts that maintain alignment and prevent drift across different model versions.

Set up batch processing observability by establishing clear ownership protocols and monitoring measurable metrics. This ensures contract alignment and tracks drift across model versions during production execution.

Yes, this model serving approach works for both online inference and batch processing. It automates design and hardening by defining explicit serving surfaces, rollout governance, and observability across teams.

Explicit contracts and safety gates are needed for ML model serving to prevent production incidents and data drift. They ensure measurable observability, clear ownership, and safe rollouts across all deployed model versions.