Service
MLOps
A model that isn't monitored is a liability. We build the deployment and observability layer alongside the model itself.
What It Is
Model deployment, monitoring, and retraining pipelines that keep models honest after launch. A model that isn't monitored is a liability. We build the deployment and observability layer alongside the model itself.
Key Capabilities
- CI/CD for machine learning models
- Feature store design and implementation
- Model monitoring and drift detection
- Automated retraining pipelines
- Experiment tracking and reproducibility
- Model registry and versioning
The SEF Framework Applied to MLOps
Every mlops engagement runs through Discover (assess current state), Design (target architecture), Engineer (build and test), Activate (deploy and monitor), and Scale (expand as needs grow) — see the full Sadbhagy Engineering Framework.