Model deployment
Serving models as fast, scalable APIs or batch jobs, with zero-downtime updates.
Data & Infrastructure
MLOps & cloud infrastructure
From a notebook to a service your business can rely on.
Versioned models, automated retraining, monitoring and a sensible cloud bill — the engineering that keeps a model accurate in month twelve, not just on demo day.
Models are only useful in production.
MLOps is the engineering practice that keeps machine learning working after the first demo: versioned data and models, automated training pipelines, reliable deployment, and monitoring that notices when accuracy slips because the world has changed. It is the difference between a model that impressed once and one your business depends on.
We set up infrastructure proportional to your needs — a single well-monitored API for one model, or a full platform for a team shipping many — on AWS, Google Cloud, Azure or your own servers, with attention to reliability, security and the monthly bill.
MLOps & Cloud Infrastructure
What we offer
Training pipelines
Reproducible, automated training with versioned data, code and models.
Monitoring & drift detection
Tracking latency, errors, accuracy and data drift, with alerts before users notice problems.
Cloud architecture
Secure, scalable infrastructure on AWS, GCP or Azure, defined as code.
GPU & cost optimisation
Right-sizing compute, using spot instances and model optimisation to cut inference costs.
On-premise & private deployment
Running models inside your own network when data cannot leave it.
When it’s the right fit
- You have models that work in notebooks but not in production
- Deploying a new model version is manual, slow or risky
- Nobody notices when a model’s accuracy degrades
- Your cloud or GPU bill is growing faster than usage
How we deliver
- 01Current-state reviewHow models are trained, shipped and watched today, and where that breaks or costs too much.
- 02Pipeline setupReproducible training and deployment with versioned data, code and model artefacts.
- 03MonitoringDashboards and alerts for latency, errors, prediction quality and data drift.
- 04Cost & reliability tuningAutoscaling, right-sized instances and lighter models wherever accuracy allows.
Technology
Tools we use for MLOps & Cloud Infrastructure
- AWS
- Google Cloud
- Azure
- Docker
- Kubernetes
- Terraform
- MLflow
- GitHub Actions
Frequently asked questions
Which cloud providers do you work with?
AWS, Google Cloud and Microsoft Azure, as well as on-premise servers. We recommend based on your existing setup, data location and costs.
Can you take over a model another team built?
Yes. We review the model and code, then build the deployment, monitoring and retraining around it — or tell you if it needs rework first.
How do you keep infrastructure costs under control?
By sizing compute to real load, autoscaling, using cheaper instance types where safe, and making models smaller and faster when possible.
Next step
Tell us what you want to build.
A free 30-minute consultation with an engineer — no obligation, reply within one business day.
Contact us