Get Your Model Out of the Notebook
A model that only runs in a notebook isn't shipped. We build the pipeline that gets it into production and keeps it there.
The Problem
Most Models Never Leave the Notebook
Training a model is the easy part. Getting it into production — versioned, monitored, retrained when it drifts — is where most projects stall. Without a real deployment pipeline, a working model sits on someone’s laptop indefinitely, or goes live with no way to tell when it starts failing.
What's Included
CI/CD for ML —
Pipelines that test and deploy model changes the way you'd deploy any other code.
Model versioning —
Knowing exactly which model version is live, and rolling back cleanly when you need to.
Deployment pipelines —
Getting a trained model into a real serving environment, not a one-off script.
Monitoring and retraining triggers —
Catching drift before it shows up as a bad prediction in production.
How It Works
Pipeline assessment —
What you have today, and where it actually breaks down.
Deployment architecture —
Designing the pipeline around your model, your traffic, and your infrastructure.
Build —
Building the pipeline, tested end to end.
Monitoring handover —
Set up so your team can see what's happening after launch, not just at launch.
Why moventAI
A model sitting in a notebook doesn’t help anyone. We think about deployment from the start, not as an afterthought once the model already “works” — because a model that’s 95% accurate and never ships is worth less than one that’s 80% accurate and actually running.
Who It's For
- Teams with a trained model that’s stuck pre-production.
- Data science teams that need an engineering partner to handle the deployment side.