Model versioning in ML Python

Introduction

Model versioning helps keep track of different versions of a machine learning model so you can compare, update, or go back to earlier versions easily.

When you improve your model and want to save the new version without losing the old one.

When you want to test different model settings and compare their results.

When you deploy a model and need to update it later without breaking the system.

When working in a team so everyone knows which model version is being used.

When you want to reproduce past results exactly by using the same model version.

Syntax

ML Python

Save model: model.save('model_v1.h5')
Load model: model = keras.models.load_model('model_v1.h5')

Model files can be named with version numbers or dates to keep track.

Use clear and consistent naming to avoid confusion.

Examples

Save the first version of the model to a file named 'model_v1.h5'.

ML Python

model.save('model_v1.h5')

Save an improved model as version 2.

ML Python

model.save('model_v2.h5')

Load the first version of the model to use or test it.

ML Python

model = keras.models.load_model('model_v1.h5')

Sample Model

This code trains a simple model twice, saving two versions. Then it loads each version and shows their accuracy to compare.

ML Python

import tensorflow as tf
from tensorflow import keras

# Create a simple model
model = keras.Sequential([
    keras.layers.Dense(10, activation='relu', input_shape=(5,)),
    keras.layers.Dense(1, activation='sigmoid')
])

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# Dummy data
import numpy as np
X = np.random.random((100, 5))
y = np.random.randint(2, size=(100, 1))

# Train model version 1
model.fit(X, y, epochs=2, verbose=0)

# Save version 1
model.save('model_v1.h5')

# Train more for version 2
model.fit(X, y, epochs=2, verbose=0)

# Save version 2
model.save('model_v2.h5')

# Load version 1 and evaluate
model_v1 = keras.models.load_model('model_v1.h5')
loss1, acc1 = model_v1.evaluate(X, y, verbose=0)

# Load version 2 and evaluate
model_v2 = keras.models.load_model('model_v2.h5')
loss2, acc2 = model_v2.evaluate(X, y, verbose=0)

print(f"Version 1 accuracy: {acc1:.4f}")
print(f"Version 2 accuracy: {acc2:.4f}")

Important Notes

Always save your model after training to keep a record of that version.

Use version numbers or timestamps in filenames to organize models clearly.

Loading an older model version lets you reproduce past results exactly.

Summary

Model versioning means saving different copies of your model as you improve it.

It helps you compare, update, and reproduce models easily.

Use clear file names and save models regularly during development.

Practice

1. What is the main purpose of model versioning in machine learning?

easy

A. To speed up the training process

B. To save different copies of a model as it improves

C. To reduce the size of the dataset

D. To increase the number of features automatically

Solution

1. Step 1: Understand the concept of model versioning

   Model versioning means saving multiple copies of a model at different stages to track improvements.

2. Step 2: Identify the main goal

   The main goal is to keep versions so you can compare, update, or reproduce models easily.

3. Final Answer:

   To save different copies of a model as it improves -> Option B

4. Quick Check:

   Model versioning = saving model copies [OK]

Hint: Think: Why save multiple models? To track changes [OK]

Common Mistakes:

• Confusing versioning with speeding training
• Thinking it reduces dataset size
• Assuming it adds features automatically

2. Which of the following is the correct way to save a model version in Python using joblib?

easy

A. joblib.store(model, 'model_v1.pkl')

B. joblib.save(model, 'model_v1.pkl')

C. joblib.dump(model, 'model_v1.pkl')

D. joblib.write(model, 'model_v1.pkl')

Solution

1. Step 1: Recall joblib syntax for saving models

   The correct function to save a model with joblib is dump.

2. Step 2: Match the correct function and filename

   Using joblib.dump(model, 'model_v1.pkl') saves the model correctly.

3. Final Answer:

   joblib.dump(model, 'model_v1.pkl') -> Option C

4. Quick Check:

   joblib save function = dump [OK]

Hint: Remember: joblib uses dump to save, load to load [OK]

Common Mistakes:

• Using save instead of dump
• Using store or write which don't exist
• Confusing load and dump functions

3. Consider this Python code snippet for saving model versions:

import joblib

model_v1 = {'accuracy': 0.8}
model_v2 = {'accuracy': 0.85}

joblib.dump(model_v1, 'model_v1.pkl')
joblib.dump(model_v2, 'model_v2.pkl')

loaded_model = joblib.load('model_v2.pkl')
print(loaded_model['accuracy'])

What will be the printed output?

medium

A. 0.8

B. model_v2.pkl

C. Error: file not found

D. 0.85

Solution

1. Step 1: Understand the saving and loading process

   Two models are saved with different filenames: 'model_v1.pkl' and 'model_v2.pkl'.

2. Step 2: Identify which model is loaded and printed

   The code loads 'model_v2.pkl' and prints its accuracy value, which is 0.85.

3. Final Answer:

   0.85 -> Option D

4. Quick Check:

   Loaded model accuracy = 0.85 [OK]

Hint: Check which file is loaded before printing [OK]

Common Mistakes:

• Printing accuracy of model_v1 instead of model_v2
• Confusing filename with accuracy value
• Assuming loading causes error

4. You have this code to save model versions but it throws an error:

import joblib

model = {'accuracy': 0.9}
joblib.dump(model, model_v1.pkl)

What is the error and how to fix it?

medium

A. model_v1.pkl is not quoted as a string; add quotes

B. joblib.dump requires three arguments; add one more

C. model must be a trained model object, not a dict

D. joblib module is not imported correctly

Solution

1. Step 1: Identify the syntax error in the filename

   The filename model_v1.pkl is used without quotes, so Python treats it as a variable, causing a NameError.

2. Step 2: Fix by adding quotes around the filename

   Change to joblib.dump(model, 'model_v1.pkl') to pass a string filename.

3. Final Answer:

   model_v1.pkl is not quoted as a string; add quotes -> Option A

4. Quick Check:

   Filename must be a string [OK]

Hint: Always quote filenames as strings in save/load functions [OK]

Common Mistakes:

• Forgetting quotes around filename
• Adding unnecessary extra arguments
• Thinking model type causes error here

5. You want to keep track of multiple model versions with their training accuracy and date. Which approach below best supports clear model versioning and easy retrieval?

hard

A. Save models with filenames like 'model_2023-06-01_acc0.85.pkl' and keep a CSV log with version info

B. Overwrite the same file 'model.pkl' every time you train a new model

C. Save models without any naming pattern and search manually later

D. Save only the best model and delete all others

Solution

1. Step 1: Understand the need for clear versioning

   Clear filenames with date and accuracy help identify model versions easily.

2. Step 2: Use a CSV log to track versions and metadata

   Keeping a CSV log allows easy retrieval and comparison of models.

3. Final Answer:

   Save models with filenames like 'model_2023-06-01_acc0.85.pkl' and keep a CSV log with version info -> Option A

4. Quick Check:

   Clear naming + log = best versioning [OK]

Hint: Use descriptive filenames plus a log file for tracking [OK]

Common Mistakes:

• Overwriting files losing old versions
• Saving without naming pattern causing confusion
• Deleting older models losing history