Model versioning in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Why is model versioning important in machine learning projects?

Choose the best reason why keeping track of different versions of a machine learning model is important.

A To avoid using any validation data during training

B To reduce the size of the training dataset automatically

C To increase the speed of model training by using older versions

D To ensure reproducibility and track changes in model performance over time

💡 Hint

Think about how you can compare models and fix issues if you keep track of versions.

Model versioning helps keep a record of changes and performance, making it easier to reproduce results and improve models safely.

❓ Predict Output

intermediate

What is the output of this model versioning code snippet?

Given the following Python code using a simple dictionary to simulate model versions, what will be printed?

ML Python

model_versions = {}

# Save version 1
model_versions['v1'] = {'accuracy': 0.85}

# Save version 2
model_versions['v2'] = {'accuracy': 0.90}

# Retrieve accuracy of version 1
print(model_versions['v1']['accuracy'])

A 0.85

B None

C KeyError

D 0.90

💡 Hint

Look at which version's accuracy is printed.

The code prints the accuracy stored under 'v1', which is 0.85.

❓ Hyperparameter

advanced

Which hyperparameter change should be tracked in model versioning for a neural network?

When saving different versions of a neural network model, which hyperparameter change is most important to track for understanding model differences?

A Learning rate used during training

B Color of the computer running the training

C Number of CPU cores in the machine

D Operating system version

💡 Hint

Think about what affects how the model learns.

The learning rate directly affects training and model performance, so it is critical to track.

❓ Metrics

advanced

Which metric is best to compare model versions for a classification task?

You have saved two versions of a classification model. Which metric is most appropriate to compare their performance?

A Time taken to write the training script

B Size of the model file in megabytes

C Accuracy on a validation dataset

D Number of lines of code in the model script

💡 Hint

Consider what measures how well the model predicts.

Accuracy on validation data shows how well the model performs on unseen data, making it the best metric for comparison.

🔧 Debug

expert

What error does this model versioning code raise?

Examine the code below that attempts to load a model version. What error will it raise?

ML Python

model_registry = {'v1': {'accuracy': 0.88}}

# Attempt to access a non-existent version
print(model_registry['v2']['accuracy'])

A IndexError

B KeyError

C TypeError

D AttributeError

💡 Hint

Check what happens when you access a dictionary key that does not exist.

Accessing 'v2' which is not in the dictionary raises a KeyError.

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