Technical debt in ML systems in MLOps - Mini Project: Build & Apply

Managing Technical Debt in ML Systems

📖 Scenario: You are working as a machine learning engineer in a team that builds ML models for predicting customer churn. Over time, the ML system has grown complex and hard to maintain. Your task is to create a simple Python script that helps identify and manage technical debt by tracking model versions and their metadata.

🎯 Goal: Build a Python script that stores model metadata, sets a threshold for maximum allowed model versions, filters out old models exceeding the threshold, and prints the list of active models. This helps keep the ML system clean and manageable.

📋 What You'll Learn

Create a dictionary called model_versions with exact keys as model version names and values as their accuracy scores.

Create a variable called max_versions set to the integer 3 to limit the number of active models.

Use a dictionary comprehension to create a new dictionary called active_models that includes only the top max_versions models by accuracy.

Print the active_models dictionary to display the current active models.

💡 Why This Matters

🌍 Real World

ML systems often accumulate many model versions and metadata, which can cause confusion and errors if not managed well. This project simulates a simple way to track and limit active models to reduce technical debt.

💼 Career

Understanding how to manage model versions and technical debt is crucial for ML engineers and MLOps specialists to maintain reliable and maintainable ML systems.

1

Create the initial model versions dictionary

Create a dictionary called model_versions with these exact entries: 'v1.0': 0.82, 'v1.1': 0.85, 'v2.0': 0.88, 'v2.1': 0.90, 'v3.0': 0.87.

MLOps

# Create the model_versions dictionary with exact version names and accuracy scores
# Your code here

Need a hint?

Use curly braces to create a dictionary with version names as keys and accuracy scores as values.

2

Set the maximum allowed model versions

Create a variable called max_versions and set it to the integer 3.

MLOps

model_versions = {'v1.0': 0.82, 'v1.1': 0.85, 'v2.0': 0.88, 'v2.1': 0.90, 'v3.0': 0.87}
# Create max_versions variable and set it to 3
# Your code here

Need a hint?

Just assign the number 3 to the variable max_versions.

3

Filter active models using dictionary comprehension

Use a dictionary comprehension to create a new dictionary called active_models that contains only the top max_versions models by accuracy from model_versions. Sort the models by accuracy in descending order and include only the top 3.

MLOps

model_versions = {'v1.0': 0.82, 'v1.1': 0.85, 'v2.0': 0.88, 'v2.1': 0.90, 'v3.0': 0.87}
max_versions = 3
# Create active_models dictionary with top 3 models by accuracy
# Your code here

Need a hint?

Use sorted() with key=lambda item: item[1] and reverse=True to sort by accuracy descending. Then slice the first max_versions items and build a dictionary comprehension.

4

Print the active models

Write a print statement to display the active_models dictionary.

MLOps

model_versions = {'v1.0': 0.82, 'v1.1': 0.85, 'v2.0': 0.88, 'v2.1': 0.90, 'v3.0': 0.87}
max_versions = 3
active_models = {k: v for k, v in sorted(model_versions.items(), key=lambda item: item[1], reverse=True)[:max_versions]}
# Print the active_models dictionary
# Your code here

Need a hint?

Use print(active_models) to show the filtered dictionary.