Engineering
MLOps without the buzzwords
Models drift, data changes, and yesterday's accuracy is no guarantee. What it really takes to keep AI working in production.
Marcus Reid·Principal Engineer·April 24, 2026·6 min read
Shipping a model is the start, not the finish. The real work is keeping it accurate, fast and cheap as the world underneath it changes.
Monitor the inputs, not just the outputs
Most model failures show up in the data first. We monitor input distributions for drift so we catch problems before accuracy visibly drops.
Make retraining boring
Retraining should be a routine, automated pipeline — not a heroic quarterly project. Reproducible data, versioned models and one-click rollback turn a scary operation into a Tuesday.
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Written by
Marcus Reid
Principal Engineer, DSME Global Links
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