Regulatory compliance (GDPR, AI Act) in MLOps - Mini Project: Build & Apply

Regulatory Compliance Setup for MLOps Pipelines

📖 Scenario: You work in a team building machine learning models that handle personal data. Your company must follow rules like GDPR and the AI Act to protect user privacy and ensure fairness.To help with this, you will create a simple compliance checklist in code. This checklist will track if your ML pipeline meets key regulatory requirements.

🎯 Goal: Build a small program that stores compliance requirements, sets a status for each, and then lists which requirements are met or not met. This helps your team quickly see if the ML pipeline follows important rules.

📋 What You'll Learn

Create a dictionary with exact compliance requirements as keys and their descriptions as values

Add a dictionary to track compliance status for each requirement

Write code to filter and list requirements that are met

Print the list of met requirements exactly as specified

💡 Why This Matters

🌍 Real World

Companies building AI models must follow laws like GDPR and the AI Act to protect users and be fair. This project shows how to track compliance in code simply.

💼 Career

DevOps and MLOps engineers often automate compliance checks in pipelines. Knowing how to represent and check compliance programmatically is a key skill.

1

Create compliance requirements dictionary

Create a dictionary called requirements with these exact keys and values:
'GDPR_data_protection': 'Ensure personal data is protected',
'AI_Act_transparency': 'Maintain transparency in AI decisions',
'GDPR_consent': 'Obtain user consent before data use',
'AI_Act_fairness': 'Prevent bias in AI models'

MLOps

# Your code here

Need a hint?

Use a Python dictionary with the exact keys and values given.

2

Add compliance status dictionary

Create a dictionary called status with the same keys as requirements and set their values exactly as:
'GDPR_data_protection': True,
'AI_Act_transparency': False,
'GDPR_consent': True,
'AI_Act_fairness': False

MLOps

requirements = {
    'GDPR_data_protection': 'Ensure personal data is protected',
    'AI_Act_transparency': 'Maintain transparency in AI decisions',
    'GDPR_consent': 'Obtain user consent before data use',
    'AI_Act_fairness': 'Prevent bias in AI models'
}
# Your code here

Need a hint?

Match the keys exactly and set True or False as shown.

3

Filter met compliance requirements

Create a list called met_requirements that contains keys from status where the value is True. Use a for loop with variables req and met to iterate over status.items().

MLOps

requirements = {
    'GDPR_data_protection': 'Ensure personal data is protected',
    'AI_Act_transparency': 'Maintain transparency in AI decisions',
    'GDPR_consent': 'Obtain user consent before data use',
    'AI_Act_fairness': 'Prevent bias in AI models'
}

status = {
    'GDPR_data_protection': True,
    'AI_Act_transparency': False,
    'GDPR_consent': True,
    'AI_Act_fairness': False
}

# Your code here

Need a hint?

Use a for loop over status.items() and append keys where value is True.

4

Print met compliance requirements

Write a print statement to display the list met_requirements exactly as it is.

MLOps

requirements = {
    'GDPR_data_protection': 'Ensure personal data is protected',
    'AI_Act_transparency': 'Maintain transparency in AI decisions',
    'GDPR_consent': 'Obtain user consent before data use',
    'AI_Act_fairness': 'Prevent bias in AI models'
}

status = {
    'GDPR_data_protection': True,
    'AI_Act_transparency': False,
    'GDPR_consent': True,
    'AI_Act_fairness': False
}

met_requirements = []
for req, met in status.items():
    if met:
        met_requirements.append(req)

# Your code here

Need a hint?

Use print(met_requirements) to show the list.