Data & MLOps Foundations
The pipelines, feature stores and monitoring that let models stay accurate after launch, not just at demo time.
12–20 weeks
Typical timeline
$65k
Starting fee
Data & MLOps
Practice
02 · Overview
How we approach it
A model’s accuracy on launch day is the easiest number to hit and the least useful one. The real test is whether it’s still accurate six months later, after the data has drifted and the world it was trained on has moved on.
We build the foundations that make that possible: versioned data pipelines, feature stores your team can reuse across models, deployment tooling with a real rollback path, and drift monitoring that alerts before accuracy visibly degrades, not after a business user notices.
03 · What’s included
Every
Data & MLOps Foundations
engagement
includes:
Scoped precisely after a workflow walkthrough, then written into the statement of work. Nothing on this list is optional.
Data pipeline & feature store design
Model training & evaluation infrastructure
Deployment & rollback tooling
Drift & performance monitoring
Cost and latency budgets
04 · What it typically delivers
6.4 pts
average improvement in forecast accuracy (MAPE)
99.95%
average uptime across systems we operate
16 weeks
average time to production
05 · Timeline
12–20 weeks
scoped after a walkthrough.
The exact plan is written after we walk the workflow with the people who do it today. These are the phases it usually runs through.
Platform audit
Sources, pipelines, gaps
Wk 1–3
Pipelines & lineage
Versioned, tested, documented
Wk 3–11
Deployment & monitoring
Models as code, alerts from day one
Wk 11–17
Handoff
Runbooks and a named owner
Wk 17–20
06 · In production
See it
running somewhere real.
08 · Questions about this service
Questions
about this service.
Anything else, ask on the call. We answer before you sign, not after.
—
Do you work with our existing cloud and data stack?
—
What triggers a retraining cycle?
—
Who owns this after handoff?
09 · Start
Tell us the workflow.
We’ll scope the rest.
We’ll tell you what this service looks like for it, what it would take and what it would be worth.