Services
MLOps
The engineering discipline that keeps models reliable after launch day.
Overview
MLOps applies software engineering and operations practice to machine learning. It covers versioning of data and models, automated training and testing, deployment, monitoring and rollback.
Without it, models become fragile artefacts that only their authors can run. With it, retraining is routine, regressions are caught early and regulators or auditors can see how a prediction was produced.
Problems we help with
- Manual, undocumented deployment steps that only one person understands
- Silent model degradation as data and behaviour change
- No lineage from a prediction back to the data and code that produced it
- Slow release cycles because every change needs bespoke testing
Capabilities
Training and deployment pipelines
Automated, repeatable workflows from raw data to serving endpoint.
Experiment and model registry
Tracked runs, versioned artefacts and approval stages.
Monitoring and drift detection
Data quality, performance and drift dashboards with alert thresholds.
Testing for ML
Data validation, model unit tests, shadow deployments and canary releases.
Serving infrastructure
Batch, streaming and low-latency serving with autoscaling.
Governance and lineage
Model cards, audit logs and access policies.
How we approach it
- 01
Assess maturity
Review how models move from notebook to production today.
- 02
Establish the paved road
Create templates and platform components teams can reuse.
- 03
Automate quality gates
Encode tests and approvals into the pipeline.
- 04
Operate with feedback
Track service levels and feed monitoring findings back into model improvement.
Where it is applied
- Standardising deployment for a multi-team data science group
- Adding monitoring to existing production models
- Building a feature store for shared, consistent inputs
- Preparing models for internal or external audit
Outcomes to aim for
These are goals we design towards, not guarantees.
- Shorter, safer release cycles
- Early warning when models drift
- Reproducible results and clear lineage
- Less dependence on individual engineers
Frequently asked questions
Bring us a decision you want to improve.
Tell us about the problem and the data you have. We will reply with a candid view of where we can help.