Reproducible training pipelines
Pinned data versions, pinned dependencies, and a single command that reproduces any published model.
AI & Machine Learning
MLOps & Model Deployment
Getting models off laptops: reproducible training, versioned artefacts, automated evaluation gates, and deployment you can roll back.
Overview
A model that only one person can retrain is a liability. We put the training, evaluation, and release path into version control so any engineer on your team can reproduce a result and ship a replacement.
This is often the work that rescues a stalled project. The model was fine; the path from experiment to production did not exist.
What you receive
- Training pipeline in CI with pinned data and dependency versions
- Model registry with promotion and rollback
- Containerised inference service and load test results
- Monitoring and alerting configuration
- Handover documentation for your engineers
Typical stack
- Docker
- GitHub Actions
- MLflow
- AWS
- Terraform
- Grafana
Capabilities
What this covers in practice
Evaluation gates in CI
Automated checks that block a release when accuracy or latency regresses against the current model.
Serving and scaling
Containerised inference with autoscaling, batching, and clear latency budgets per endpoint.
Drift and cost monitoring
Alerting on input distribution shift and on inference spend, before either becomes a surprise.
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Read moreWant to talk through a mlops & model deployment project?
Send us the shape of the problem and we'll come back with a scoped approach, a timeline, and an honest read on what's achievable.