ML lifecycle stages in MLOps - Mini Project: Build & Apply

ML Lifecycle Stages

📖 Scenario: You are working as a junior MLOps engineer. Your team wants to document the main stages of the machine learning lifecycle in a simple Python program. This will help new team members understand the process clearly.

🎯 Goal: Create a Python dictionary that lists the main stages of the ML lifecycle with their descriptions. Then, add a variable to select a stage to focus on. Next, write code to extract the description of the selected stage. Finally, print the description to show the result.

📋 What You'll Learn

Create a dictionary called ml_lifecycle with exact keys and values for stages

Add a variable called selected_stage with the exact stage name to focus on

Write code to get the description of selected_stage from ml_lifecycle

Print the description of the selected stage exactly

💡 Why This Matters

🌍 Real World

Documenting ML lifecycle stages helps teams understand and follow the process clearly, improving collaboration and project success.

💼 Career

MLOps engineers often create tools and documentation to manage ML workflows, making this knowledge essential for clear communication and automation.

1

Create the ML lifecycle dictionary

Create a dictionary called ml_lifecycle with these exact entries: 'Data Collection' mapped to 'Gathering raw data from various sources.', 'Data Preparation' mapped to 'Cleaning and transforming data for modeling.', 'Model Training' mapped to 'Building the machine learning model.', 'Model Evaluation' mapped to 'Assessing model performance.', and 'Deployment' mapped to 'Releasing the model to production.'

MLOps

# Create the ml_lifecycle dictionary with the exact stages and descriptions
# Your code here

Need a hint?

Use curly braces {} to create the dictionary. Each key and value must be exactly as given.

2

Add a selected stage variable

Create a variable called selected_stage and set it to the string 'Model Training'.

MLOps

ml_lifecycle = {
    'Data Collection': 'Gathering raw data from various sources.',
    'Data Preparation': 'Cleaning and transforming data for modeling.',
    'Model Training': 'Building the machine learning model.',
    'Model Evaluation': 'Assessing model performance.',
    'Deployment': 'Releasing the model to production.'
}
# Create the selected_stage variable with the value 'Model Training'
# Your code here

Need a hint?

Assign the exact string 'Model Training' to the variable selected_stage.

3

Get the description of the selected stage

Create a variable called stage_description and set it to the value from ml_lifecycle for the key stored in selected_stage.

MLOps

ml_lifecycle = {
    'Data Collection': 'Gathering raw data from various sources.',
    'Data Preparation': 'Cleaning and transforming data for modeling.',
    'Model Training': 'Building the machine learning model.',
    'Model Evaluation': 'Assessing model performance.',
    'Deployment': 'Releasing the model to production.'
}
selected_stage = 'Model Training'
# Get the description for the selected_stage from ml_lifecycle
# Your code here

Need a hint?

Use the dictionary access syntax with the variable selected_stage as the key.

4

Print the description of the selected stage

Write a print statement to display the value of stage_description.

MLOps

ml_lifecycle = {
    'Data Collection': 'Gathering raw data from various sources.',
    'Data Preparation': 'Cleaning and transforming data for modeling.',
    'Model Training': 'Building the machine learning model.',
    'Model Evaluation': 'Assessing model performance.',
    'Deployment': 'Releasing the model to production.'
}
selected_stage = 'Model Training'
stage_description = ml_lifecycle[selected_stage]
# Print the stage_description
# Your code here

Need a hint?

Use print(stage_description) to show the description.