ML Engineering & MLOps
Models that stay accurate in production — training pipelines, deployment, and monitoring.
FAMA MLOps covers feature stores, CI for models, A/B releases, and drift detection. Your data science team ships faster without breaking ops.
What we deliver
- Training pipelines
- Model registry
- Deployment patterns
- Monitoring & drift
- GPU & cost management
- Runbooks
How we work
01.Discover & scope
We align on the business outcome, users, constraints, and what success looks like before design or code starts.
02.Design & build
UX, engineering, and QA move in parallel with reviews you can see — on the devices your customers use.
03.Launch & stay
Go-live includes handover docs and support options. FAMA can remain on hosting, releases, and optimisation.
Revolutionizing your digital presence through engineering and craft
FAMA MLOps covers feature stores, CI for models, A/B releases, and drift detection. Your data science team ships faster without breaking ops.
A model that worked in a notebook but fails silently in prod is the most expensive kind of AI project.
FAMA
Tell us the outcome you need. FAMA will propose a stack you can actually run.
A short brief is enough — we reply with scope, team shape, and a first milestone.
