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.