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LogicWise

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

Other services

Tell us what you are trying to deploy

A 30-minute call, no charge and no sales script. Describe the cloud or AI problem, and we will tell you honestly whether we are the right people for it.