Services
Machine Learning
A useful prediction depends on more than choosing a model.
Machine Learning
Connect the information to a useful decision
We look at whether the historical data represents the decision you need to make, define a meaningful baseline and evaluate the result against it. Deployment planning includes how new data, changing patterns and model performance will be reviewed.
Questions behind the data
- Data preparation & feature engineering
- Model training, tuning & validation
- Deployment pipeline & API delivery
- Model performance monitoring
- Retraining & continuous improvement
In practice
Machine Learning: a practical example
Define the prediction, the source records and the consequence of an incorrect result. Compare the proposed approach with a simple baseline using representative examples. Review unusual cases separately and agree who monitors the result once the data or working process changes.
An illustrative scenario to discuss when planning your project.