Category M: Inference Monitoring

This category covers monitoring and auditing inference pipelines for production ML systems.

Recipes

Key Concepts

Inference Explainability

Understanding why a model made a particular prediction is critical for:

• Debugging model behavior
• Regulatory compliance (GDPR, AI Act)
• Building user trust
• Identifying bias and drift

Hash Chain Auditing

Cryptographic hash chains provide:

• Tamper-evident logs of all predictions
• Reproducibility verification
• Compliance audit trails
• Data lineage tracking

Stack Integration

use apr_cookbook::explainable::IntoExplainable;
use aprender::linear_model::LinearRegression;
use entrenar::monitor::inference::{
    path::LinearPath, InferenceMonitor, RingCollector,
};

// Train and wrap with explainability
let model = LinearRegression::new();
// ... fit model ...
let explainable = model.into_explainable();

// Create monitored inference
let collector: RingCollector<LinearPath, 64> = RingCollector::new();
let mut monitor = InferenceMonitor::new(explainable, collector);

// Predictions are now traced
let output = monitor.predict(&features, 1);
let trace = monitor.collector().recent(1)[0];
println!("{}", trace.path.explain());

Toyota Way Principles

• Jidoka: Built-in quality through explainability
• Genchi Genbutsu: "Go and see" via audit trails
• Kaizen: Continuous improvement through monitoring