Delivery & Integration – Bridge Data Science to Production in 4–6 Weeks
Get your model out of the lab and into production with a pragmatic integration plan and a release that actually ships. I coordinate between Data Science, Engineering, and Product so the right APIs, data flows, and operational guardrails are in place before you launch.
The Process
- Integration Kickoff (Day 1)
- Architecture & Flow Mapping (Week 1–2)
- Delivery Plan Creation (Week 2)
- Implementation Oversight (Weeks 2–5)
- Release & Post-Launch Checks (Week 6)
Review model output, API requirements, data pipelines, and deployment constraints. Confirm Definition of Done and success metrics.
Map integration touchpoints, dependencies, and observability requirements. Identify key failure modes and mitigation strategies.
Build a milestone-based delivery plan with parallel workstreams for development, QA, and deployment.
Coordinate cross-functional work, keep dependencies unblocked, and ensure integrations meet spec.
Ship to production with logging, monitoring, and rollback plans in place. Post-launch QA to confirm stability and performance.
What You Get
- Integration Map — architecture diagram of data, API, and workflow touchpoints.
- Delivery Schedule — milestone-based plan tied to your release cadence.
- Definition of Done — mutually agreed criteria for successful launch.
- Observability Checklist — logging, monitoring, and alerting requirements.
FAQ
Can you work with our internal dev team?
Yes — I often serve as the bridge between data science and platform/app teams.
Do you handle vendor integrations?
Yes — I coordinate with third-party API and MLOps vendors as part of the plan.
What if our model needs retraining?
We can scope retraining into the plan or hand off to your data science team.
Will you manage QA?
I’ll ensure QA coverage is in the plan and coordinate with your QA resources.
Pricing Guidance
Most Delivery & Integration engagements range from $15,000–$25,000 depending on complexity, systems involved, and release risk profile.
Let’s Get Started
Make your model reliable, observable, and ready for prime time.