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Production Deployment

Scale and monitor regression models in production.

Model Storage​

CREATE TABLE production_model AS
SELECT
    'revenue_model_v1' as model_name,
    coefficient,
    std_error,
    CURRENT_TIMESTAMP as created_at
FROM anofox_statistics_ols(
    'all_historical_data',
    'revenue',
    ARRAY['marketing_spend', 'team_size']
);

Batch Prediction​

CREATE TABLE forecast_results AS
SELECT
    date,
    marketing_spend,
    predicted_revenue,
    lower_95,
    upper_95
FROM upcoming_periods
CROSS JOIN LATERAL anofox_statistics_model_predict(
    'production_model',
    upcoming_periods,
    0.95
);

Performance Monitoring​

CREATE VIEW model_health AS
SELECT
    DATE_TRUNC('week', date) as week,
    COUNT(*) as n_obs,
    SQRT(AVG((actual - predicted)^2)) as rmse,
    CASE WHEN rmse > 300000 THEN 'ALERT' ELSE 'OK' END as status
FROM predictions
GROUP BY week
ORDER BY week DESC;

Retraining​

-- Monthly refit
CREATE TABLE model_v2 AS
SELECT * FROM anofox_statistics_ols(
    'recent_data_last_90_days',
    'revenue',
    ARRAY['marketing_spend', 'team_size']
);

-- Compare AIC
SELECT aic FROM model_v1 vs model_v2;
-- Deploy v2 if better

Next Steps​

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