Model Lifecycle
Induction, training, retraining, reinduction and monitoring: how a Hybrid Intelligence model evolves without becoming opaque.
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01
The Hybrid Intelligence Induction Process
Induction is the process that learns the structure of a Hybrid Intelligence model. It analyses the data and optional knowledge, identifies useful feature interactions, constructs modules and partitions, generates local rules and packages the trained Explainable Neural Network (XNN) within an Explanation Structure Model (ESM).
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02
Training and Retraining XNNs
Training optimises the parameters of an induced Explainable Neural Network (XNN). Retraining reuses a compatible module, partition and rule structure with updated data or objectives. The lifecycle retains a trainable XNN and produces a serving XNN for runtime use.
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03
Retraining vs Reinduction
Retraining updates parameters within a compatible induced Explainable Neural Network (XNN) structure. Reinduction learns a new structure. The decision depends on whether the model's modules, partitions, inputs and assumptions still describe the problem being solved.
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04
Monitoring Drift, Activation Paths and Explanation Stability
Explainable Neural Network (XNN) monitoring can observe not only inputs and outputs, but also which modules, partitions and rules are used. Changes in activation paths and attributions provide an additional view of drift and explanation stability.