Model drift detection in ML Python - Model Pipeline Trace

Model Pipeline - Model drift detection

This pipeline shows how a machine learning model can detect when its predictions start to become less accurate over time, a problem called model drift. It tracks data changes and model performance to alert when retraining is needed.

Data Flow - 6 Stages

1Data Collection

10000 rows x 10 columns→Collect new data samples over time→10000 rows x 10 columns

New customer data with 10 features like age, income, and purchase history

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2Preprocessing

10000 rows x 10 columns→Clean data and normalize features→10000 rows x 10 columns

Normalized age and income values between 0 and 1

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3Feature Extraction

10000 rows x 10 columns→Select important features for prediction→10000 rows x 5 columns

Selected features: age, income, purchase frequency, last purchase days, customer rating

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4Model Prediction

10000 rows x 5 columns→Use trained model to predict target→10000 rows x 1 column

Predicted probability of customer churn

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5Performance Monitoring

10000 rows x 1 column→Compare predictions with actual outcomes→Summary statistics

Accuracy dropped from 90% to 75% over last 1000 samples

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6Drift Detection

Summary statistics→Apply statistical tests to detect data or performance drift→Drift alert (True/False)

KS test p-value < 0.01 indicates feature distribution drift

Training Trace - Epoch by Epoch

Loss
0.7 |****
0.6 |*** 
0.5 |**  
0.4 |**  
0.3 |*   
0.2 |    
    +-----
     1 10 Epoch

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.60	Model starts learning basic patterns
5	0.40	0.78	Model improves accuracy steadily
10	0.30	0.85	Model converges with good accuracy

Prediction Trace - 4 Layers

Layer 1: Input Features

Layer 2: Model Prediction

Layer 3: Performance Check

Layer 4: Drift Detection

Model Quiz - 3 Questions

What does a model drift alert indicate in this pipeline?

A The input data has more columns than before

B The model has finished training

C The model's predictions are becoming less accurate over time

D The model's accuracy is 100%

Key Insight

Model drift detection helps keep machine learning models reliable by watching for changes in data or performance. When drift is detected, retraining the model with fresh data can restore accuracy.