Deployment & MLOps for Prototypes

Practical steps, checklists, and a playbook to keep prototypes reliable, observable, and ready to graduate with lightweight MLOps, monitoring, and rollback plans.


Playbook

MLOps for Prototypes — Practical Playbook (Lightweight, Actionable)

A practical, step-by-step playbook to keep AI prototypes reliable, observable, and ready to graduate. Focuses on lightweight MLOps: minimal deployment checks, instrumentation, monitoring and drift detection, retraining triggers, rollback plans, ownerable governance, cost and data safeguards, and a clear post-trial decision template.

Members: