Monitoring model performance in ML Python - Model Pipeline Trace

Model Pipeline - Monitoring model performance

This pipeline shows how a machine learning model's performance is tracked during training and after deployment. It helps us see if the model is learning well and if it keeps working correctly over time.

Data Flow - 6 Stages

1Raw Data Input

1000 rows x 10 columns→Collect data with features and labels→1000 rows x 10 columns

Each row has 10 features like age, income, and label is 'buy' or 'not buy'

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2Data Preprocessing

1000 rows x 10 columns→Clean data, fill missing values, normalize features→1000 rows x 10 columns

Missing ages filled with average, incomes scaled between 0 and 1

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3Train/Test Split

1000 rows x 10 columns→Split data into training and testing sets→800 rows x 10 columns (train), 200 rows x 10 columns (test)

800 rows used to train, 200 rows saved to check model later

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4Model Training

800 rows x 10 columns→Train model on training data→Trained model

Model learns patterns to predict 'buy' or 'not buy'

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5Performance Monitoring

200 rows x 10 columns (test data), Trained model→Evaluate model predictions and calculate metrics→Accuracy, Precision, Recall, F1-score values

Model predicts 180 correct out of 200, accuracy = 90%

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6Deployment Monitoring

New incoming data→Track model predictions and real outcomes over time→Performance metrics over time

Accuracy drops from 90% to 75%, alert triggered

Training Trace - Epoch by Epoch

Loss
0.7 |*****
0.6 |**** 
0.5 |***  
0.4 |**   
0.3 |*    
    +------------
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.60	Model starts learning, accuracy is low
2	0.50	0.72	Loss decreases, accuracy improves
3	0.40	0.80	Model is learning well
4	0.35	0.85	Loss keeps decreasing, accuracy rising
5	0.30	0.88	Training converging, good performance

Prediction Trace - 4 Layers

Layer 1: Input Layer

Layer 2: Hidden Layer (ReLU activation)

Layer 3: Output Layer (Sigmoid activation)

Layer 4: Threshold Decision

Model Quiz - 3 Questions

What does a decreasing loss during training indicate?

A The model is learning and improving

B The model is forgetting data

C The data is incorrect

D The model is overfitting immediately

Key Insight

Monitoring model performance helps us understand if the model is learning well during training and if it continues to work well after deployment. Tracking metrics like loss and accuracy over time allows us to detect problems early and keep the model reliable.