Stewardship

Models should not be deployed without a monitoring strategy. To this end, we provide a template that addresses data drift and model drift. These steps are in addition to the data testing that is built into the feature engineering pipeline.

1 Key terms

• Ground Truth: the true answer
• Predicted or Inferred value or label: the model or the machines best guess
• Training data: a sufficient sample of the training data set used compare against the new (unseen) data
• Unseen data: data that has arrived after the model has been trained and deployed. The data in production.

2 Walk through

• Clone the ‘model seedling’ template