Model versioning in ML Python - Model Pipeline Trace

Model Pipeline - Model versioning

Model versioning helps keep track of different versions of a machine learning model as it improves. It is like saving different drafts of a school essay to see which one works best.

Data Flow - 7 Stages

1Data Collection

1000 rows x 10 columns→Collect raw data for training→1000 rows x 10 columns

Rows of customer info with 10 features each

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2Data Preprocessing

1000 rows x 10 columns→Clean and normalize data→1000 rows x 10 columns

Normalized numbers, missing values filled

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3Feature Engineering

1000 rows x 10 columns→Create new features from existing data→1000 rows x 12 columns

Added two new columns combining features

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4Model Training v1

1000 rows x 12 columns→Train first model version→Model v1 saved

Model v1 with accuracy 75%

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5Model Training v2

1000 rows x 12 columns→Train improved model version→Model v2 saved

Model v2 with accuracy 80%

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6Model Evaluation

Test set 200 rows x 12 columns→Evaluate model versions→Performance metrics for each version

Model v1 accuracy 75%, Model v2 accuracy 80%

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7Model Deployment

Selected model version→Deploy chosen model to production→Model serving live predictions

Model v2 deployed for real-time use

Training Trace - Epoch by Epoch

Loss
0.65 |****
0.50 |***
0.40 |**
0.35 |**
0.30 |*
Epochs ->

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.60	Starting training, loss high, accuracy low
2	0.50	0.70	Loss decreased, accuracy improved
3	0.40	0.75	Model learning well
4	0.35	0.78	Loss continues to drop, accuracy rising
5	0.30	0.80	Training converging, good accuracy

Prediction Trace - 5 Layers

Layer 1: Input Layer

Layer 2: Hidden Layer 1 (ReLU)

Layer 3: Hidden Layer 2 (ReLU)

Layer 4: Output Layer (Softmax)

Layer 5: Prediction

Model Quiz - 3 Questions

Why do we save multiple versions of a model during training?

A To compare performance and pick the best one

B To confuse users with many models

C Because models get lost easily

D To use all versions at the same time

Key Insight

Model versioning helps track improvements by saving different model versions. Watching training loss go down and accuracy go up shows the model is learning. Softmax at the output turns numbers into probabilities, making predictions easy to understand.