The gap between a model that works in a notebook and one that works in production is where most value is lost. We close it: reproducible training, the same feature logic at training and serving time, deployment that can be rolled back, and monitoring for the drift that silently degrades accuracy months later.
Data & Analytics
Machine Learning Analytics
Models in production, monitored, with a way to retrain.
Overview
What the engagement covers
- Reproducible training pipelines with versioned data and code
- A feature store, so training and serving cannot diverge
- Deployment with shadow running and staged rollout
- Monitoring for data drift, prediction drift and accuracy decay
- A retraining path that does not need its original author
What you leave with
- Models serving real traffic rather than sitting in notebooks
- Degradation noticed by monitoring, not by a business complaint
- Retraining that is routine rather than a rebuild
Data & Analytics
The rest of this practice
Machine Learning Analytics, scoped to your estate
Tell us where you are and what it has to be worth. We will come back with a scope, a sequence and a number.