MLOps
3articles in this topic.
The silent revenue drop: how drift actually breaks production models
Drift does not crash your service or page your on-call. It quietly degrades a model for weeks while every dashboard stays green, until finance asks why a revenue line is bleeding. This is the operational war story the KS-test tutorials skip.
ML platform build vs buy: a decision framework for 2026
Most teams default to one of three answers — Databricks, SageMaker, or 'we'll roll our own.' All three are wrong for the wrong team. Here's how to pick by team size, model variety, and latency budget — with the war stories that explain why.
Model monitoring in 2026: from accuracy to behavior drift
Classical ML monitoring tracks accuracy decay. LLM monitoring tracks something stranger — the model itself silently changing underneath you. Here's what production observability looks like when the failure modes don't fit the dashboards you built five years ago.