Workflow Deep Dive

LinkedIn Draft — Workflow (2026-03-24) {{opener}} End‑to‑end MLOps retraining loop: reliability is in the guardrails Auto‑retraining is easy to wire. Making it safe in production is the hard part: data drift, silent label shifts, and rollback semantics. What usually bites later: A “better” offline model can degrade live KPIs due to skew (training vs serving features) and traffic shift. Unversioned data/labels make incident RCA impossible — you can’t reproduce what trained the model. Promotion without canary + rollback turns retraining into a weekly outage generator. My default rule: No model ships without: dataset/version lineage, shadow/canary evaluation, and a one‑click rollback path. When I’m sanity-checking this, I usually do: Track dataset + features with DVC/LakeFS + model registry (MLflow/SageMaker Registry) for auditable promotion. Monitor drift + performance slices with Prometheus/Grafana + alert on trend , not single spikes. Deep dive (stable link): https://neeraja-portfolio-