Random seed management in MLOps - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a random seed in machine learning?

A random seed is a starting point number used to initialize a random number generator. It ensures that the sequence of random numbers is the same every time, helping to reproduce results.

beginner

Why is random seed management important in MLOps?

It helps make experiments repeatable and results consistent, which is crucial for debugging, comparing models, and deploying reliable machine learning systems.

beginner

How do you set a random seed in Python for reproducibility?

Use the command <code>import random; random.seed(42)</code> for the built-in random module, and <code>import numpy as np; np.random.seed(42)</code> for NumPy random functions.

beginner

What can happen if you do not manage random seeds in your ML pipeline?

Your model training and evaluation results may vary each time you run the code, making it hard to debug, compare, or trust the results.

intermediate

Name two common libraries where random seed should be set for full reproducibility in Python ML projects.

The random module and numpy library. Also, for deep learning, libraries like TensorFlow or PyTorch have their own seed settings.

What does setting a random seed do?

A Makes random numbers predictable and repeatable

B Increases randomness in data

C Removes randomness completely

D Generates new random numbers every time

Setting a random seed makes the sequence of random numbers predictable and repeatable, which helps reproduce results.

Which Python command sets the seed for NumPy's random number generator?

A seed(42)

B random.seed(42)

C numpy.seed(42)

D np.random.seed(42)

The correct command is np.random.seed(42) to set the seed for NumPy's random number generator.

Why is random seed management critical in MLOps?

A To speed up training

B To ensure reproducible and consistent results

C To increase model accuracy automatically

D To reduce data size

Random seed management ensures reproducible and consistent results, which is essential in MLOps for reliable model development.

If you do not set a random seed, what is likely to happen?

A Your results may vary each run

B Your results will be the same every time

C Your code will not run

D Your model will always overfit

Without setting a random seed, the results may vary each time you run the code due to different random values.

Which of these is NOT a place to set a random seed for full reproducibility?

A Python random module

B NumPy random module

C Operating system kernel

D TensorFlow or PyTorch

The operating system kernel is not where you set random seeds for ML reproducibility; seeds are set in the programming libraries.

Explain what a random seed is and why it matters in machine learning projects.

Think about how random numbers are generated and why repeating experiments is important.

Describe how you would manage random seeds in a Python-based ML pipeline to ensure reproducible results.

Consider all sources of randomness in your code.