Random seed management in MLOps - Mini Project: Build & Apply

Random Seed Management in MLOps

📖 Scenario: You are working on a machine learning project where reproducibility is important. You want to make sure that every time you run your training script, the results are the same. This is done by setting a random seed.

🎯 Goal: Learn how to set a random seed in Python to ensure reproducible results in machine learning workflows.

📋 What You'll Learn

Create a variable to hold the random seed value

Set the random seed using the random module

Set the random seed using the numpy module

Print the random seed value to confirm it is set

💡 Why This Matters

🌍 Real World

In machine learning projects, setting random seeds ensures that experiments can be repeated with the same results, which is important for debugging and sharing work.

💼 Career

Data scientists and MLOps engineers use random seed management to maintain reproducibility and reliability in machine learning pipelines.

1

Create a random seed variable

Create a variable called seed and set it to the integer 42.

MLOps

# Your code here

Need a hint?

Use a simple assignment statement like seed = 42.

2

Set the random seed for the random module

Import the random module and set its seed using the variable seed.

MLOps

seed = 42
# Your code here

Need a hint?

Use import random and then random.seed(seed).

3

Set the random seed for the numpy module

Import the numpy module as np and set its random seed using the variable seed.

MLOps

seed = 42
import random
random.seed(seed)
# Your code here

Need a hint?

Use import numpy as np and then np.random.seed(seed).

4

Print the random seed value

Write a print statement to display the value of the variable seed.

MLOps

seed = 42
import random
random.seed(seed)
import numpy as np
np.random.seed(seed)
# Your code here

Need a hint?

Use print(seed) to show the seed value.