BeamSpark Claims Lab
Claims ML deployment studio · Seoul
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MLOps architecture

Production monitoring design lab

Turn vague “watch the model” requests into dashboards, alerts, and ownership that survive rotation.

Duration
7 weeks
Format
Remote with optional war-room week
Indicative fee
₩22,100,000
Discuss this package

Overview

We inventory signals you can realistically collect, define alert thresholds that avoid pager fatigue, and connect monitoring to your existing incident channels.

What is included

  • Metric catalogue grouped by data, model, and business health
  • Sampling strategy for expensive ground-truth checks
  • Runbook templates integrated with your ITSM tool
  • Ownership RACI that names on-call rotation assumptions
  • Synthetic transaction tests where permitted
  • Quarterly review agenda for the governance forum
  • Decommission checklist for retired models

Outcomes you can inspect

  • Fewer false-positive alerts in the first month post-launch
  • Clear escalation when business KPIs diverge from model metrics
  • Living document linking dashboards to accountable roles
RC

Rina Cho

MLOps engineer with a background in regulated batch pipelines for large carriers.

FAQ

Can this run without a feature store?

Yes, with pragmatic compromises. We document trade-offs explicitly.

Pager expectations

We recommend conservative thresholds initially; aggressive auto-tuning is out of scope.

What we do not monitor

We do not monitor social media sentiment about your brand as a proxy for model quality.

Experience notes

“Pager noise dropped after we adopted their tiered alert story. Still tuning synthetic tests—internal network quirks made that slower than hoped.”