Why governance builds trust in ML systems in MLOps - See It in Action

Why governance builds trust in ML systems

📖 Scenario: You are part of a team managing machine learning models in a company. To keep models reliable and trustworthy, you need to track their performance and decisions carefully. This helps everyone trust the system and use it safely.

🎯 Goal: Build a simple Python program that stores ML model performance data, sets a threshold for acceptable accuracy, filters models that meet this threshold, and prints the trusted models. This simulates governance by showing how only models that pass checks are trusted.

📋 What You'll Learn

Create a dictionary called models with model names as keys and their accuracy scores as values

Create a variable called accuracy_threshold and set it to 0.8

Use a dictionary comprehension to create a new dictionary called trusted_models that includes only models with accuracy greater than or equal to accuracy_threshold

Print the trusted_models dictionary

💡 Why This Matters

🌍 Real World

In real ML projects, governance ensures models are safe and reliable before deployment. Tracking model performance helps catch problems early and maintain trust.

💼 Career

ML engineers and MLOps specialists use governance practices to monitor models, meet regulations, and build confidence among users and stakeholders.

1

Create the models dictionary

Create a dictionary called models with these exact entries: 'ModelA': 0.75, 'ModelB': 0.82, 'ModelC': 0.90, 'ModelD': 0.65

MLOps

# Create the models dictionary with exact accuracy values
# Your code here

Need a hint?

Use curly braces to create a dictionary. Put model names as keys and accuracy as values.

2

Set the accuracy threshold

Create a variable called accuracy_threshold and set it to 0.8

MLOps

models = {'ModelA': 0.75, 'ModelB': 0.82, 'ModelC': 0.90, 'ModelD': 0.65}
# Set the accuracy threshold to 0.8
# Your code here

Need a hint?

Just assign 0.8 to the variable accuracy_threshold.

3

Filter trusted models

Use a dictionary comprehension to create a new dictionary called trusted_models that includes only models with accuracy greater than or equal to accuracy_threshold. Use for model, accuracy in models.items() in your comprehension.

MLOps

models = {'ModelA': 0.75, 'ModelB': 0.82, 'ModelC': 0.90, 'ModelD': 0.65}
accuracy_threshold = 0.8
# Create trusted_models dictionary with models having accuracy >= accuracy_threshold
# Your code here

Need a hint?

Use dictionary comprehension with if accuracy >= accuracy_threshold to filter.

4

Print the trusted models

Write a print statement to display the trusted_models dictionary

MLOps

models = {'ModelA': 0.75, 'ModelB': 0.82, 'ModelC': 0.90, 'ModelD': 0.65}
accuracy_threshold = 0.8
trusted_models = {model: accuracy for model, accuracy in models.items() if accuracy >= accuracy_threshold}
# Print the trusted_models dictionary
# Your code here

Need a hint?

Use print(trusted_models) to show the filtered dictionary.