Machine Learning
Models that survive contact with production
A model that scores well offline and rots in production is a liability. We build the boring parts properly — feature pipelines, drift monitoring, retraining triggers — so accuracy holds after launch.
Capabilities
What this actually includes
The concrete pieces of work, so you can tell what you are buying rather than inferring it.
Feature pipelines
Reproducible transformations shared between training and serving, so the model sees the same shape of data in both.
Training and tuning
Versioned experiments with tracked parameters and artefacts, so a result can be reproduced months later.
Serving infrastructure
Batch and low-latency online inference, autoscaled, with graceful degradation to a heuristic when the model is unavailable.
Drift and quality monitoring
Input distribution and prediction quality tracked continuously, with alerts wired to the people who can act on them.
Retraining automation
Scheduled or trigger-based retraining with automatic evaluation against the incumbent before promotion.
Explainability
Per-prediction attribution where decisions affect people, and model documentation that stands up to review.
How we work
The sequence we follow
Frame the decision
We start from the action the prediction will drive. If no decision changes, the model should not be built.
Establish a baseline
A simple rule or heuristic first. It sets the bar the model has to clear and often turns out to be good enough.
Build the pipeline
Feature engineering and data validation before modelling, because that is where most production failures originate.
Train, evaluate, challenge
Candidate models scored against the baseline on held-out and time-split data, with error analysis on the segments that matter.
Deploy with monitoring
Shadow deployment, then staged rollout, with drift monitoring and a documented rollback in place before traffic moves.
Outcomes
What good looks like
Illustrative targets from engagements of this shape. Yours get agreed up front and measured.
0%
lift over the heuristic baseline at launch
0 days
of accuracy held without manual intervention
0 min
median time from drift alert to on-call notice
Toolkit
What we build with
Chosen per engagement against your constraints — never a house stack applied regardless of fit.
Modelling
- scikit-learn
- XGBoost
- PyTorch
- Prophet
- statsmodels
Pipelines
- dbt
- Airflow
- Dagster
- Feature stores
Tracking
- MLflow
- Weights & Biases
- Model registries
Serving
- FastAPI
- Triton
- SageMaker
- Batch scoring jobs
Questions
Things clients ask first
Less than most teams assume for a first useful model, and more than they assume for a reliable one. We can usually tell you after a week with your data whether the signal is there at all.
Keep exploring
Related capabilities
Tell us what you are trying to build
A short call with an engineer, not a sales team. If we are not the right fit we will say so and point you somewhere better.