Event-Enriched AI Features¶

Raw features often fail to capture the full context of a domain. Event-enriched features go beyond simple counts or averages by embedding temporal, causal, and relational signals directly from the event stream.

Because Event Sourcing preserves every fact in chronological order, you can design features that reflect not only what happened, but also when, why, and in relation to what.

Why Event Enrichment Matters¶

AI models trained only on aggregated or static features can miss subtle but important signals:

Temporal patterns – time between actions, seasonality, or trends.
Causal sequences – chains of events that tend to lead to certain outcomes.
Cross-entity relationships – interactions between people, resources, and processes.

Enriching features with these signals can dramatically improve predictive power, explainability, and model robustness.

Types of Event-Enriched Features¶

Common enrichment strategies include:

Lag features – measure time elapsed since a key event.
Event frequencies – count specific event types within a sliding time window.
Sequence embeddings – encode ordered event sequences for sequence models.
Derived states – compute status values from past events (e.g., active loans, outstanding fees).
Context joins – combine events with external data for richer context.

These can be applied individually or in combination, depending on the model's needs.

AI/ML Considerations¶

Event-enriched features must be:

Point-in-time correct – values should reflect only information available at the prediction moment.
Reproducible – given the same events, the feature computation must yield the same result.
Interpretable – stakeholders should understand how features were derived and why they matter.

Careful feature engineering ensures that enrichment adds signal without introducing leakage or bias.

Practical Example (Library Domain)¶

A model predicting late returns could use:

Lag: days since the member's last loan.
Frequency: number of overdue returns in the past 6 months.
Derived state: current number of active loans.
Temporal pattern: borrowing spikes during summer holidays.
Cross-entity relationship: correlation between member's loan history and demand patterns for similar titles.

By combining these, the model gains a richer, more context-aware view than raw aggregates could provide.

Best Practices¶

Start with a clear hypothesis for why each enrichment might improve predictions.
Validate enriched features against a baseline model to confirm added value.
Keep enrichment logic modular so features can evolve independently.
Document the enrichment process for transparency and auditability.

Event-enriched features transform your model inputs from blunt statistics into nuanced signals, helping AI systems act with greater accuracy and relevance.

Next up: AI Model Drift Detection with Event Sourcing – monitor model performance over time and adapt when patterns change.