Ship models like you ship code
A repeatable path from notebook to production: model registry, automated tests, staged rollouts and drift monitoring, built in your own cloud.
Overview
Many data science teams train good models and then struggle to release them. Deployment is a ticket to another team, rollback is a manual copy, and nobody notices when input data shifts.
We set up a model registry, CI/CD pipelines for models, a serving layer with canary releases, and monitoring for drift and prediction quality. Your data scientists release through a pull request and can roll back in one step.
Signs you need this
- Nobody can say which model version is serving traffic right now
- Rolling back a bad model means rebuilding an image by hand
- Model accuracy dropped and you found out from a customer
- Training runs cannot be reproduced because data and code were not versioned together
What you get
- Model registry
- MLflow or a cloud-native registry holding every model with its training data reference, code version and metrics.
- Model CI/CD pipeline
- Automated tests for data schema, model quality thresholds and serving behaviour before any release.
- Serving platform
- KServe, BentoML or managed endpoints, with autoscaling and a standard way to expose each model.
- Canary and rollback
- New versions take a small share of traffic first, with automatic rollback when error or latency limits are breached.
- Drift and quality monitoring
- Dashboards and alerts on input drift, prediction distribution and, where labels arrive later, real accuracy.
How the work runs
- 1
Current state review
1 to 2 weeks
We trace how one existing model goes from training to production and list every manual step.
- 2
Registry and pipeline
2 to 4 weeks
We set up the registry, versioned training runs and a release pipeline for a first model.
- 3
Serving and rollout
2 to 3 weeks
We deploy the serving layer with canary releases and rollback, then move the first model onto it.
- 4
Monitoring and second model
2 to 3 weeks
We add drift monitoring, then your team moves a second model through the process with us watching.
Timings depend on the size of your estate. The assessment fixes them before any build starts.
What you can check afterwards
- Every production model links back to its training data, code commit and metrics in the registry
- A model release happens through a merged pull request, not a manual deploy
- Rollback to the previous version is one command and has been tested
- Drift alerts fire in a test where input data is deliberately shifted
Tools we work with
- MLflow
- KServe
- BentoML
- Amazon SageMaker
- Azure Machine Learning
- Vertex AI
- GitHub Actions
- Argo CD
- DVC
- Evidently
- Prometheus
From our work
North American client, delivered by the OpsBreak team
Rebuilding a model release pipeline to ship in hours
Model deployments cut from 3 weeks to 4 hours
Questions buyers ask
- Do we have to change how our data scientists work?
- A little. They keep their notebooks for exploration but commit training code to a repository. The pipeline handles the rest.
- Does training data have to move?
- No. Training and serving run in your cloud account next to the data. We reference datasets by location and version, not by copying them.
- Will this work with our existing Kubernetes cluster?
- Usually yes. If you already run Kubernetes, we install serving components there. If not, managed endpoints may be simpler.
- What happens after handover?
- Your team owns the pipeline and registry. We can stay on for a few hours a month to review releases or help with the next model.
Talk to us about MLOps and model deployment.
Tell us what you run and where. We reply within one business day with questions or a quote.