Machine Learning Engineering
The models that quietly make money.
Forecasting, ranking, risk scoring and anomaly detection, deployed with the feature pipelines, monitoring and retraining that keep accuracy from decaying.
- Drift-monitored
- Reproducible training
- Explainable by design
What it is
The unglamorous models still win
Gradient-boosted trees on clean features beat a language model at demand forecasting, and will keep doing so. The interesting question is rarely which architecture — it is whether the pipeline that feeds it is trustworthy.
We build the whole path: feature pipelines with training-serving parity, reproducible training runs, staged deployment, and drift monitoring that tells you a model has stopped working before your customers do.
- Training-serving parity enforced
- Reproducible, versioned training runs
- Shadow and canary deployment
- Drift and performance monitoring
Model families we work with
- Gradient boosting
- Deep learning where warranted
- Time-series models
- Embedding retrieval
Forecast error reduction
Median improvement over incumbent models
Of models drift-monitored
Monitoring ships with the model, not after
Capabilities
Across the model lifecycle
Feature engineering
Feature pipelines shared between training and serving, so the model sees the same distribution in production that it learned on.
- Feature store
- Point-in-time correctness
- Backfill tooling
Model development
Baseline first, complexity only where it earns its keep, with every experiment tracked and reproducible.
- Experiment tracking
- Baseline benchmarking
- Hyperparameter search
Deployment
Shadow mode, canary and staged rollout so a new model proves itself on real traffic before it decides anything.
- Shadow deployment
- Canary rollout
- Instant rollback
Monitoring
Input drift, prediction drift and business-outcome tracking, with a retraining trigger rather than a calendar reminder.
- Drift detection
- Outcome tracking
- Automated retraining
Explainability
Feature attribution and decision records where a regulator, an auditor or a customer may reasonably ask why.
- Feature attribution
- Decision logging
- Fairness testing
Use cases
What this looks like in practice
Deployments we have built or scoped, with the sector they landed in.
Demand forecasting
Inventory and capacity planning against seasonality and promotions.
- Retail & Commerce
Fraud detection
Real-time scoring with the explainability a dispute process requires.
- Financial Services
Route optimisation
Predicted transit times feeding live dispatch decisions.
- Logistics & Supply Chain
Stack
Technologies we use here
Sectors
Where this lands first
Also relevant
Related services
Assurance
How we keep this honest
The commitments that matter when the system is making or shaping decisions.
Fairness tested
Where a model affects people, we test performance across segments and report the gaps rather than the average.
Reproducible
Any production prediction can be traced to the exact code, data and parameters that produced it.
FAQ
Questions we are asked
Yes, and it is a common starting point. The work is usually rebuilding the feature pipeline for serving rather than the model itself — notebooks tend to use data that will not exist at prediction time.
Get a model out of the notebook
Tell us what it predicts and we will scope the path to production.