A model that works in a notebook breaks in production: versions drift, costs creep up and nobody sees failures.
13 / MLOps
MLOps
Keep models running, measured and improving after launch.
Deploy, monitor and scale AI models in production without the manual upkeep.
We audit how models move from a notebook to production today, then automate the weakest step first, usually deployment or monitoring. Each model gets a version, a test gate and a rollback path before we add anything else.
What the build includes
A complete production workflow.
Automated deployment pipeline
A build that tests, packages and releases a model to staging and production without manual steps.
Model registry and versioning
Every model stored with its data version, parameters and metrics, so any release can be reproduced or rolled back.
Monitoring and drift alerts
Dashboards and alerts for latency, errors, input drift and quality, sent to the right person.
Usage and spend tracking
Per-model reporting of compute and API use, so surprises show up early.
Where it applies
Typical mlops work.
- →Moving notebook models into a service
- →Catching quality drops after a data change
- →Scheduling retraining runs
- →Comparing a new model against the live one
How a project runs
Four steps, no surprises.
- 01
Discovery call
A short call about the goal and the first workflow or feature worth building.
- 02
Written scope
What is built, which tools it touches, what a person approves, and the timeline, agreed before work starts.
- 03
Build and review
Short iterations connected to your tools, reviewed with your team before anything ships.
- 04
Launch and handoff
Go-live with documentation and an owner on your side. You own what we build.
Technology and fit
Built with the right tools, not every tool.
The exact stack follows the workflow, security requirements, existing systems, and deployment environment.
Technologies
DockerKubernetesAWSGoogle CloudPython
Industries
Expected outcomes
- →Releases that are routine, not risky
- →Quick rollback when a model misbehaves
- →Early warning when inputs change
- →Visible compute spend
- →Less reliance on one person who knows the setup
Common questions
Before we scope it.
We only have a few models. Is this overkill?
We scale the setup to match. For a small team that may be one pipeline and one dashboard rather than a full platform.
Which cloud do you work with?
AWS, Google Cloud and Azure, plus Kubernetes or plain containers. We work in your accounts so you keep ownership.
Does this cover LLM applications too?
Yes. We track prompts, model versions, test results and token usage the same way we track classic models.
Keep exploring
Related services and technologies.
Free Audit
Get my free Audit ↗Know what to build first.
We review the workflow, recommend the first build, and give you a rough price range and timeline before you commit.