Deploying ML Models Without the Drama

Most production ML incidents we've debugged had nothing to do with model architecture — they came from missing input validation, silent data drift, or a training/serving skew nobody caught before launch.

Our deployment checklist starts before the first line of serving code: define the input contract, log every prediction with its inputs, and set explicit thresholds for when a model needs retraining.

Treat the model as one component in a larger system with the same rigor you'd apply to any other service — versioned, monitored, and rollback-ready.