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.