Model versioning in TensorFlow - Model Pipeline Trace

Model Pipeline - Model versioning

This pipeline shows how a machine learning model is trained and saved with different versions. Model versioning helps keep track of improvements and changes over time.

Data Flow - 7 Stages

1Data Loading

1000 rows x 10 columns→Load raw data from CSV file→1000 rows x 10 columns

[[5.1, 3.5, 1.4, ..., 0], [4.9, 3.0, 1.4, ..., 0], ...]

↓

2Data Preprocessing

1000 rows x 10 columns→Normalize features to range 0-1→1000 rows x 10 columns

[[0.52, 0.7, 0.14, ..., 0], [0.49, 0.6, 0.14, ..., 0], ...]

↓

3Train/Test Split

1000 rows x 10 columns→Split data into 80% train and 20% test→Train: 800 rows x 10 columns, Test: 200 rows x 10 columns

Train sample: [0.52, 0.7, 0.14, ..., 0], Test sample: [0.48, 0.65, 0.13, ..., 0]

↓

4Model Training

Train: 800 rows x 10 columns→Train neural network model→Trained model with weights

Model weights updated after each epoch

↓

5Model Saving (Version 1)

Trained model→Save model as version 1→Model file saved as 'model_v1'

File path: './models/model_v1/'

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6Model Retraining

Train: 800 rows x 10 columns→Train model with more epochs or data→Improved trained model

Model weights improved after more training

↓

7Model Saving (Version 2)

Improved trained model→Save model as version 2→Model file saved as 'model_v2'

File path: './models/model_v2/'

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	0.70	Model starts learning with moderate loss and accuracy
2	0.50	0.80	Loss decreases and accuracy improves
3	0.40	0.85	Model continues to improve
4	0.35	0.88	Training converges with better accuracy
5	0.30	0.90	Final epoch shows best performance for version 1

Prediction Trace - 4 Layers

Layer 1: Input Layer

Layer 2: Hidden Layer (ReLU activation)

Layer 3: Output Layer (Softmax)

Layer 4: Prediction

Model Quiz - 3 Questions

Why do we save the model as different versions?

A To keep track of improvements and changes over time

B To make the model run faster

C To reduce the size of the model file

D To avoid training the model

Key Insight

Model versioning is important to save and compare different trained models. It helps track progress and choose the best model for deployment.