Marketing Mix Modeling Acceleration
Internal MLOps application to speed Marketing Mix Modeling project workflows for consulting teams, including a later migration onto client Databricks.
- Role
- AI engineer
- Context
- Professional
- Capabilities
- Observability, Data engineering, Workflow automation
Overview
Marketing mix modelling has a long, repetitive project shape: data in, model fit, charts out, next geography. This work was an internal MLOps application to make that shape less manual for consulting teams, later refactored onto a client Databricks workspace.
I led the platform design and the migration work. The econometric methodology and the client’s model specification were not mine.
Problem
MMM projects spent too much calendar time on glue: environments, data handoffs, run tracking. The interesting statistics were blocked on the uninteresting plumbing.
Approach
A workflow app around the modelling loop, then a move onto the client’s Databricks estate so runs lived where the data already was. Exact dates, brand names and any “hours saved” figures stay off this stealth page.
Tradeoffs and limitations
Accelerating a workflow is not the same as improving a model. I do not claim adoption, revenue or a speed-up factor. Time-saving figures are not published here.
Confidentiality
Stealth / capability-only. No client name, no screenshots, no unverified metrics.