Data versioning (DVC) in ML Python - Model Pipeline Trace

Model Pipeline - Data versioning (DVC)

Data versioning with DVC helps track changes in datasets over time, just like saving different versions of a document. This makes it easy to manage data for machine learning projects and reproduce results.

Data Flow - 4 Stages

1Initial dataset

1000 rows x 10 columns→Raw data collected from sensors→1000 rows x 10 columns

Each row has sensor readings like temperature, humidity, and timestamp

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2Data versioning with DVC

1000 rows x 10 columns→DVC tracks dataset snapshot and stores metadata→1000 rows x 10 columns (versioned)

DVC saves a version labeled 'v1' with a hash to identify this exact data

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3Data update

1200 rows x 10 columns→New data added and versioned with DVC→1200 rows x 10 columns (versioned)

DVC saves new version 'v2' with added rows for new sensor readings

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4Model training input

1200 rows x 10 columns→Load specific data version for training→1200 rows x 10 columns

Model trains on data version 'v2' ensuring reproducibility

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	0.6	Model starts learning with initial data version
2	0.5	0.72	Loss decreases as model improves
3	0.4	0.8	Model accuracy increases steadily
4	0.35	0.85	Training converges with stable improvement
5	0.3	0.88	Final epoch shows best performance

Prediction Trace - 4 Layers

Layer 1: Input data version load

Layer 2: Feature scaling

Layer 3: Model prediction

Layer 4: Thresholding

Model Quiz - 3 Questions

What does DVC primarily help with in machine learning projects?

A Improving model accuracy automatically

B Tracking different versions of datasets

C Visualizing model training loss

D Generating synthetic data

Key Insight

Data versioning with DVC is essential for managing datasets in machine learning. It ensures that models train and predict on consistent data versions, making experiments reproducible and trustworthy.