Solutions
Machine Learning
Prediction is useful when it changes a decision for the better.
A workflow across the whole team
We discuss the historical data and the point at which the prediction is needed, comparing a model with a straightforward baseline and planning how performance will be reviewed.
Parts of the proposed journey
- Predictive analytics & forecasting models
- Classification & anomaly detection
- Feature engineering & data preparation
- Model training, tuning & validation
- API-based integration into existing systems
In practice
Machine Learning: a practical example
Compare a proposed model with a simple baseline using records that were not used to fit it. Review incorrect outputs and changes in input conditions with the task owner. Agree how performance is monitored and when the team should review or stop using the model.
An illustrative scenario to discuss when planning your project.