MLOps & Deployment Services
Get models out of notebooks and into production, then keep them running. We build the pipelines, monitoring, and rollback paths that turn a trained model into a service your team can trust.
of machine learning models never reach production
Most models die between the notebook and the customer. The data science work is done, but nobody owns the pipeline, the deployment, or the monitoring that keeps a model healthy after launch. MLOps closes that gap. It is the difference between a model that worked once in a demo and a model that keeps making correct predictions six months later, without someone manually checking it every week. When an embedding model is retrained upstream, automated drift alerts and one-click rollback are what stop a silent ten-point accuracy drop from ever reaching customers.
Inside MLOps & Deployment
MLOps and deployment is the engineering work that takes a trained machine learning model and turns it into a live, monitored service. It covers packaging the model, automating retraining, deploying it behind an API or batch pipeline, and watching it in production for drift and failure. Instead of a model that works once on a laptop, you get a system that keeps working as data and traffic change.
Pipeline design — we build the automated steps that take a model from training to a packaged, deployable artifact.
Deployment architecture — we choose between real-time API, batch, or streaming deployment based on how the model is actually used.
CI/CD for models — we set up automated testing and release pipelines so new model versions ship safely, not by hand.
Monitoring and drift detection — we track prediction quality and input data over time so you know when a model needs retraining.
Rollback and versioning — we keep every model version traceable and ready to roll back if a new release underperforms.
Infrastructure and cost — we right-size the compute behind the model so it scales with demand without burning budget at idle.
Is this right for you?
This service fits best when you recognise yourself below.
Data science teams with a model that works in a notebook but has never shipped.
Engineering teams inheriting a model with no deployment or monitoring in place.
Companies running models in production manually, with no automated retraining or alerts.
Organizations scaling from one model to several and needing a repeatable deployment pattern.
The problems behind the brief
Models stuck in notebooks
A model that only runs on a data scientist's laptop delivers no business value. We build the pipeline that gets it into production.
No idea when a model goes stale
Data drifts and accuracy quietly drops. We set up monitoring that flags degradation before it shows up as a business problem.
Manual deployment, manual risk
Hand-rolled deployments break in ways nobody can reproduce. We automate the release process so every deployment is consistent and reversible.
Retraining that never happens
Models trained once and never updated drift further from reality every month. We automate retraining on a schedule or a trigger.
Infrastructure costs that do not match usage
Over-provisioned GPU instances run idle around the clock. We size and scale infrastructure to match real traffic patterns.
A clear, repeatable process
No mystery. You always know what happens this week and what comes next.
We review your current model, training process, and infrastructure, and confirm the deployment pattern that fits how it will be used.
We build the deployment pipeline, packaging, and CI/CD automation, with model versioning and rollback in place from the start.
We set up monitoring, drift detection, and alerting, then test failure scenarios before the model carries real traffic.
We deploy to production, hand over runbooks and dashboards, and confirm the team can operate the system without us.
Deliverables
Concrete outputs you keep — not just a conversation.
What good looks like
A model running in production, not stuck in a notebook.
Automated retraining that keeps accuracy from drifting unnoticed.
Deployment time cut from weeks of manual work to a repeatable pipeline.
Clear alerts before model quality becomes a business problem.
The stack behind the work
We pick tools to fit your needs, never vendor relationships.
MLOps
- MLflow
- Kubeflow
Infrastructure
- Docker
- Kubernetes
- Terraform
Cloud
- AWS SageMaker
CI/CD
- GitHub Actions
Monitoring
- Prometheus
Common questions about MLOps & Deployment
Straight answers to the questions we hear most.
Still have questions? Talk to our team
The natural next step
MLOps keeps a model healthy once it exists. If the model itself still needs to be built around your data and workflow, Custom AI Applications covers that ground first. If you need AI features wired into a product you already run, AI Integration is the better starting point.
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