Champion-challenger model comparison in MLOps - Mini Project: Build & Apply

Champion-Challenger Model Comparison

📖 Scenario: You work in a team that builds machine learning models. Your team wants to compare two models to see which one performs better on new data. This is called the champion-challenger model comparison. The champion is the current best model, and the challenger is a new model you want to test.You will write a simple Python program to compare the accuracy of two models on a test dataset.

🎯 Goal: Build a Python script that stores the accuracy scores of two models, sets a threshold for improvement, compares the scores, and prints which model is the champion or if the challenger should replace the champion.

📋 What You'll Learn

Create a dictionary called model_accuracies with two entries: 'champion' with value 0.85 and 'challenger' with value 0.88.

Create a variable called improvement_threshold and set it to 0.02.

Write an if statement to check if the challenger accuracy is greater than the champion accuracy plus the improvement threshold.

Print 'Challenger model replaces champion.' if the challenger is better by the threshold, otherwise print 'Champion model remains.'.

💡 Why This Matters

🌍 Real World

Teams often compare machine learning models to pick the best one for deployment. This process helps improve product quality and user experience.

💼 Career

Understanding champion-challenger comparisons is key for roles in MLOps, data science, and machine learning engineering where model performance tracking is essential.

1

Create model accuracy data

Create a dictionary called model_accuracies with these exact entries: 'champion': 0.85 and 'challenger': 0.88.

MLOps

# Create the model_accuracies dictionary with champion and challenger accuracies
# Your code here

Need a hint?

Use curly braces {} to create a dictionary with keys and values.

2

Set improvement threshold

Create a variable called improvement_threshold and set it to 0.02.

MLOps

model_accuracies = {'champion': 0.85, 'challenger': 0.88}
# Set the improvement_threshold variable to 0.02
# Your code here

Need a hint?

Use a simple assignment statement to create the variable.

3

Compare model accuracies

Write an if statement that checks if model_accuracies['challenger'] is greater than model_accuracies['champion'] + improvement_threshold.

MLOps

model_accuracies = {'champion': 0.85, 'challenger': 0.88}
improvement_threshold = 0.02
# Write an if statement to compare challenger and champion accuracies with the threshold
# Your code here

Need a hint?

Use an if-else block to compare the values and assign the result message.

4

Print the comparison result

Write a print statement to display the result variable.

MLOps

model_accuracies = {'champion': 0.85, 'challenger': 0.88}
improvement_threshold = 0.02
if model_accuracies['challenger'] > model_accuracies['champion'] + improvement_threshold:
    result = 'Challenger model replaces champion.'
else:
    result = 'Champion model remains.'
# Print the result variable
# Your code here

Need a hint?

Use print(result) to show the message on the screen.