ML Model Deployment
A machine learning model that lives in a Jupyter notebook creates no business value. We specialise in taking trained models from experimentation into production, reliably, scalably, and with full observability so you know exactly how your models are performing in the real world.
MLOps & Deployment Services
- Model Serving Infrastructure, REST API endpoints for real-time inference using FastAPI, TorchServe, TensorFlow Serving, or Triton Inference Server, optimised for low latency and high throughput.
- Batch Inference Pipelines, Scheduled large-scale prediction runs on millions of records using distributed computing (Spark, Dask, Ray), with results delivered to your data warehouse.
- Model Versioning & Registry, Structured model registry with versioning, rollback capability, and A/B testing infrastructure so you can safely promote new model versions.
- Containerisation & Orchestration, Docker images and Kubernetes deployments for model servers, with auto-scaling based on inference request volume.
- Feature Stores, Centralised feature computation and serving with tools like Feast or Tecton to ensure training-serving consistency and eliminate feature drift.
- Model Monitoring & Drift Detection, Real-time monitoring of prediction distributions, input feature drift, and business KPIs, with alerting when models degrade and automated retraining triggers.
Our MLOps Approach
We treat ML deployment like software deployment: version-controlled, tested, automated, and observable. Every model goes through a staging environment with shadow mode testing (serving real traffic without acting on predictions) before promotion to production. We instrument every inference endpoint with latency percentiles, error rates, and prediction distribution metrics.
Our deployment pipelines are integrated with your existing CI/CD tooling, so model updates follow the same review and approval process as code changes, giving your team full control over what runs in production.
Why Production ML Is Different
- Models can silently degrade as the real world changes, monitoring catches this before it hurts your business
- Reliable serving infrastructure ensures your ML investments generate consistent ROI
- Structured versioning and rollback eliminates the risk of deploying a worse model
- Automated retraining pipelines keep models fresh without manual intervention