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

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

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

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

Hint

Use print(trusted_models) to show the filtered dictionary.

Practice

1. Why is governance important in machine learning systems?

easy

A. It helps keep the system clear and controlled

B. It makes the system run faster

C. It removes the need for data

D. It guarantees 100% accuracy

Solution

1. Step 1: Understand governance role

   Governance in ML ensures clarity and control over the system's processes and data.

2. Step 2: Compare options

   Only It helps keep the system clear and controlled correctly states governance helps keep the system clear and controlled. Other options are incorrect or unrealistic.

3. Final Answer:

   It helps keep the system clear and controlled -> Option A

4. Quick Check:

   Governance = clarity and control [OK]

Hint: Governance means clear rules and control [OK]

Common Mistakes:

• Thinking governance speeds up ML
• Believing governance removes data needs
• Assuming governance guarantees perfect accuracy

2. Which of the following is a correct governance practice in ML systems?

easy

A. Ignoring data quality checks

B. Skipping model testing before deployment

C. Documenting model decisions clearly

D. Using random data without validation

Solution

1. Step 1: Identify good governance practices

   Good governance includes clear documentation of model decisions to ensure transparency.

2. Step 2: Evaluate options

   Only Documenting model decisions clearly describes a correct governance practice. Others ignore quality or testing, which are bad practices.

3. Final Answer:

   Documenting model decisions clearly -> Option C

4. Quick Check:

   Good governance = clear documentation [OK]

Hint: Good governance means clear documentation [OK]

Common Mistakes:

• Ignoring data quality
• Skipping testing steps
• Using unvalidated data

3. Consider this scenario: An ML system logs all model changes and data versions. What is the main benefit of this governance practice?

medium

A. It helps track and fix issues quickly

B. It speeds up model training

C. It reduces the need for data

D. It guarantees model accuracy

Solution

1. Step 1: Understand logging in governance

   Logging model changes and data versions helps keep track of what was done and when.

2. Step 2: Identify benefit

   This tracking allows quick identification and fixing of problems, improving trust in the system.

3. Final Answer:

   It helps track and fix issues quickly -> Option A

4. Quick Check:

   Logging = tracking and fixing issues [OK]

Hint: Logging helps find and fix problems fast [OK]

Common Mistakes:

• Thinking logging speeds training
• Believing logging removes data needs
• Assuming logging guarantees accuracy

4. A team notices their ML system is producing biased results. Which governance step should they check first to fix this?

medium

A. Increase the model's training speed

B. Review the data collection and labeling process

C. Remove all data validation steps

D. Ignore the issue and retrain randomly

Solution

1. Step 1: Identify cause of bias

   Bias often comes from data issues like poor collection or labeling.

2. Step 2: Choose governance step to fix bias

   Reviewing data collection and labeling helps find and correct bias sources.

3. Final Answer:

   Review the data collection and labeling process -> Option B

4. Quick Check:

   Fix bias = check data process [OK]

Hint: Check data quality first to fix bias [OK]

Common Mistakes:

• Trying to speed training instead of fixing data
• Removing validation steps
• Ignoring bias issues

5. A company wants to build trust in their ML system by improving governance. Which combined approach will best achieve this?

hard

A. Use random data and deploy models without testing

B. Skip documentation, avoid tracking changes, and ignore ethical concerns

C. Focus only on speeding up model training without checks

D. Implement clear documentation, track data versions, and enforce ethical guidelines

Solution

1. Step 1: Identify key governance elements

   Clear documentation, data version tracking, and ethical guidelines are core governance practices.

2. Step 2: Evaluate combined approach

   Implement clear documentation, track data versions, and enforce ethical guidelines combines these elements to build trust effectively. Other options ignore important governance steps.

3. Final Answer:

   Implement clear documentation, track data versions, and enforce ethical guidelines -> Option D

4. Quick Check:

   Governance = documentation + tracking + ethics [OK]

Hint: Combine documentation, tracking, and ethics for trust [OK]

Common Mistakes:

• Ignoring documentation and ethics
• Skipping tracking changes
• Focusing only on speed