Machine learning & predictive systems
Turn historical and operational data into predictions, classifications and prioritisation that can be evaluated against a meaningful baseline.
Will a model improve the decision you actually need to make?
Define the target, the point at which a prediction is made and the action it will support. Establish a simple baseline before introducing complex models. Check whether the available data reflects the population and conditions in which the model will be used.
Build a repeatable pipeline for data preparation, feature creation, training and evaluation. Separate training and evaluation data in a way that reflects time, entities and deployment conditions. Investigate leakage, label uncertainty and the cost of different kinds of error.
Plan for changing inputs and changing behaviour. Connect monitoring to retraining decisions, model versioning and rollback. Give receiving teams a clear account of appropriate use, known limitations and the evidence behind the chosen model.
What to take forward
- A measurable target and credible baseline
- Repeatable training and evaluation workflow
- Deployment, monitoring and retraining approach
What should I bring to a conversation?
Bring the decision you need to make, the constraints you already know and a small, non-confidential example. We can then agree whether a resource, a focused review or a more detailed discussion is the right next step.