The Real-Time SHAP Bottleneck

SHAP (SHapley Additive exPlanations) provides model-agnostic explanations by computing feature contributions through coalitional game theory. While SHAP delivers interpretable insights, computing Shapley values at inference time introduces prohibitive latency. For tree-based models, TreeSHAP requires traversing decision paths across all trees. For neural networks, DeepSHAP or KernelSHAP involves hundreds of forward passes with perturbed inputs. This makes real-time scoring impractical when prediction latency budgets are measured in milliseconds.

Batch Precomputation Strategy

# Batch SHAP computation pipeline
import shap
from google.cloud import bigquery

def batch_compute_shap(model, X_batch, sample_ids):
    explainer = shap.TreeExplainer(model)
    shap_values = explainer.shap_values(X_batch)
    
    # Persist top-k features per sample
    explanations = []
    for sid, vals, features in zip(sample_ids, shap_values, X_batch):
        top_features = sorted(
            zip(features.columns, vals), 
            key=lambda x: abs(x[1]), 
            reverse=True
        )[:5]
        explanations.append({
            'sample_id': sid,
            'top_features': top_features,
            'timestamp': datetime.now()
        })
    
    # Write to BigQuery
    client = bigquery.Client()
    client.insert_rows_json('project.dataset.shap_explanations', explanations)

The approach decouples explanation generation from serving. During scheduled batch inference jobs, SHAP values are computed alongside predictions and the top contributing features with their attribution scores are written to BigQuery. This enables instant retrieval via simple SQL queries for dashboards or audit requirements without runtime overhead.

Monitoring Integration for Drift Detection

Persisted SHAP values serve dual purposes beyond explainability. By tracking feature importance distributions over time, shifts in which features drive predictions signal potential model drift before accuracy degrades. Sudden changes in top contributing features often indicate upstream data quality issues or concept drift. Integrated gradient methods work similarly but require framework-specific implementations. Monitoring pipelines can alert when attribution patterns deviate from baseline distributions, enabling proactive model health management.

Applied Insight: When to Precompute

Precompute explanations when prediction latency is critical and explanation access is asynchronous. This pattern works best for batch scoring pipelines, scheduled model runs, or systems where users inspect explanations separately from predictions. For truly interactive systems requiring synchronized predictions and explanations under 100ms, consider simpler explanation methods like linear approximations or feature ablation on reduced feature sets instead of full SHAP computation.