Model versioning in ML Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is model versioning in machine learning?

Model versioning is the practice of saving and tracking different versions of a machine learning model as it changes over time. It helps keep track of improvements and changes.

beginner

Why is model versioning important?

Model versioning helps to compare different models, reproduce results, fix bugs, and safely deploy updates without losing previous work.

intermediate

Name two common ways to implement model versioning.

1. Using file naming with version numbers (e.g., model_v1, model_v2). 2. Using tools like Git, DVC, or MLflow to track model changes and metadata.

intermediate

What information is typically stored with each model version?

Each model version usually stores the model file, training data info, hyperparameters, performance metrics, and date of creation.

beginner

How does model versioning help in deploying machine learning models?

It allows safe updates by keeping old versions available, helps rollback if new models fail, and tracks which version is in production.

What does model versioning help you do?

A Automatically improve model accuracy

B Increase the speed of model training

C Track and manage different versions of a model

D Reduce the size of the dataset

Model versioning is about tracking and managing different versions of a model, not about training speed or dataset size.

Which tool can be used for model versioning?

A MLflow

B Photoshop

C Excel

D PowerPoint

MLflow is a tool designed to track machine learning models and their versions.

What should NOT be stored with a model version?

A Training hyperparameters

B Personal user passwords

C Date of model creation

D Model performance metrics

Personal user passwords are sensitive data and unrelated to model versioning.

How does model versioning help during deployment?

A By allowing rollback to previous model versions

B By increasing the model's accuracy automatically

C By reducing the model's file size

D By changing the training data

Model versioning allows safe rollback to previous versions if the new model has issues.

Which is a simple way to version models?

A Ignoring model changes

B Deleting old models

C Changing model code without saving

D Naming files with version numbers like model_v1

Using version numbers in file names is a simple way to keep track of model versions.

Explain what model versioning is and why it is useful in machine learning projects.

Think about how you keep track of different drafts of a document.

Describe common methods or tools used for model versioning and what information is stored with each version.

Consider both simple and advanced ways to save models.

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