Model metadata and lineage in MLOps - Mini Project: Build & Apply

Track Model Metadata and Lineage in MLOps

📖 Scenario: You are working in a machine learning team. You want to keep track of your models' details and how they were created. This helps your team understand the model history and reproduce results easily.

🎯 Goal: Build a simple Python program that stores model metadata and lineage information in a dictionary, updates it with configuration details, processes the data, and finally prints the full model metadata and lineage.

📋 What You'll Learn

Create a dictionary to hold model metadata with exact keys and values

Add a configuration variable for model version

Use a loop to update the metadata with lineage info

Print the final metadata dictionary

💡 Why This Matters

🌍 Real World

Tracking model metadata and lineage helps teams understand model history, reproduce results, and maintain trust in machine learning systems.

💼 Career

MLOps engineers and data scientists use metadata tracking to manage models efficiently and ensure smooth collaboration.

1

Create initial model metadata dictionary

Create a dictionary called model_metadata with these exact entries: 'model_name': 'CustomerChurnModel', 'created_by': 'DataScienceTeam', and 'accuracy': 0.85.

MLOps

# Create the model_metadata dictionary with given keys and values
# Your code here

Need a hint?

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

2

Add model version configuration

Create a variable called model_version and set it to the string 'v1.0'.

MLOps

model_metadata = {
    'model_name': 'CustomerChurnModel',
    'created_by': 'DataScienceTeam',
    'accuracy': 0.85
}
# Create model_version variable with value 'v1.0'
# Your code here

Need a hint?

Assign the string 'v1.0' to the variable model_version using the equals sign.

3

Update metadata with lineage information

Use a for loop with variable key to iterate over the list ['model_version', 'training_data', 'training_date']. Inside the loop, add each key and its value from the dictionary lineage_info = {'model_version': model_version, 'training_data': 'customer_data.csv', 'training_date': '2024-06-01'} to the model_metadata dictionary.

MLOps

model_metadata = {
    'model_name': 'CustomerChurnModel',
    'created_by': 'DataScienceTeam',
    'accuracy': 0.85
}
model_version = 'v1.0'
lineage_info = {'model_version': model_version, 'training_data': 'customer_data.csv', 'training_date': '2024-06-01'}
# Use a for loop to add lineage_info items to model_metadata
# Your code here

Need a hint?

Use a for loop to go through each key and assign the corresponding value from lineage_info to model_metadata.

4

Print the complete model metadata

Write a print statement to display the model_metadata dictionary.

MLOps

model_metadata = {
    'model_name': 'CustomerChurnModel',
    'created_by': 'DataScienceTeam',
    'accuracy': 0.85
}
model_version = 'v1.0'
lineage_info = {'model_version': model_version, 'training_data': 'customer_data.csv', 'training_date': '2024-06-01'}
for key in ['model_version', 'training_data', 'training_date']:
    model_metadata[key] = lineage_info[key]
# Print the model_metadata dictionary
# Your code here

Need a hint?

Use print(model_metadata) to show the full dictionary.