Logging artifacts and models in MLOps - Mini Project: Build & Apply

Logging Artifacts and Models

📖 Scenario: You are working on a machine learning project where you want to save your trained model and some related files (called artifacts) so you can review them later or share with your team.Using a simple logging tool, you will learn how to save these important files properly.

🎯 Goal: Build a small program that logs a model file and an artifact file using a logging tool.This will help you keep track of your machine learning work in a neat and organized way.

📋 What You'll Learn

Create a dictionary to represent the model metadata

Create a variable for the artifact file path

Write a function to log the model and artifact

Print a confirmation message after logging

💡 Why This Matters

🌍 Real World

In real machine learning projects, logging models and artifacts helps teams keep track of experiments and share results easily.

💼 Career

Knowing how to log models and artifacts is a key skill for MLOps engineers and data scientists to maintain reproducibility and collaboration.

1

Create model metadata dictionary

Create a dictionary called model_info with these exact entries: 'name': 'house_price_predictor', 'version': '1.0', and 'accuracy': 0.85.

MLOps

# Create the model_info dictionary with name, version, and accuracy
# Your code here

Need a hint?

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

2

Set artifact file path

Create a variable called artifact_path and set it to the string './artifacts/feature_importance.csv'.

MLOps

model_info = {'name': 'house_price_predictor', 'version': '1.0', 'accuracy': 0.85}
# Create the artifact_path variable with the exact file path
# Your code here

Need a hint?

Use quotes to create a string and assign it to the variable.

3

Write function to log model and artifact

Define a function called log_model_and_artifact that takes two parameters: model and artifact. Inside the function, print these two lines exactly:
Logging model: {model['name']} version {model['version']}
Logging artifact file: {artifact}.

MLOps

model_info = {'name': 'house_price_predictor', 'version': '1.0', 'accuracy': 0.85}
artifact_path = './artifacts/feature_importance.csv'
# Define the log_model_and_artifact function below
# Your code here

Need a hint?

Use an f-string to include dictionary values inside the print statements.

4

Call function and print confirmation

Call the function log_model_and_artifact with model_info and artifact_path as arguments. Then, print exactly 'Logging complete!' on a new line.

MLOps

model_info = {'name': 'house_price_predictor', 'version': '1.0', 'accuracy': 0.85}
artifact_path = './artifacts/feature_importance.csv'

def log_model_and_artifact(model, artifact):
    print(f"Logging model: {model['name']} version {model['version']}")
    print(f"Logging artifact file: {artifact}")

# Call the function and print confirmation below
# Your code here

Need a hint?

Call the function with the correct variables and then print the confirmation message exactly.