Move to AWS, Azure or GCP with a landing zone and architecture built for where you are going, not a lift-and-shift of where you are.
AI & Model Deployment (MLOps)
Take a model from notebook to production with versioning, monitoring and a rollback path — and keep it working after launch day.
A model that works in a notebook is not a model in production — it needs serving infrastructure, versioning, monitoring and a plan for what happens when the input data drifts. We build the MLOps pipeline around your models: containerised serving with autoscaling, evaluation gates before anything ships, drift and performance monitoring once it is live, and automated retraining where it earns its keep. Whether it is a classical ML model, a fine-tuned model, or an LLM-based system built on the OpenAI or Anthropic APIs, the deployment gets the same rigour as the rest of your production software.
What this includes
- Model packaging, serving & inference infrastructure
- CI/CD for ML: versioning, evaluation gates, rollback
- Drift detection, monitoring & retraining pipelines
Elsewhere
Other services.
Read about Cloud Strategy & Migration
Web platforms and internal tools built on a modern stack by a small senior team, shipped in increments you can review.
Move legacy systems onto cloud-native, AI-ready foundations incrementally — no big-bang rewrite, no feature freeze.
Next step
Tell us what you are trying to deploy.
A 30-minute call, no charge and no sales script. Describe the problem and we will tell you honestly whether we are the right people for it.