Random seed management in MLOps - Step-by-Step Execution

Process Flow - Random seed management

Set random seed

↓

Initialize random generator

↓

Run stochastic process

↓

Get reproducible output

↓

Use output for training/testing

↓

Repeat with same seed -> same results

↓

Change seed -> different results

This flow shows how setting a random seed fixes the starting point of randomness, making results repeatable across runs.

Execution Sample

MLOps

import random
random.seed(42)
print(random.randint(1, 10))
print(random.randint(1, 10))

This code sets a random seed and prints two random numbers, which will be the same every time it runs.

Process Table

Step	Action	Seed State	Random Number Generated	Output
1	Set seed to 42	Seed=42	N/A	No output
2	Generate first random int	Seed=42 (updated internally)	Random int between 1-10	2
3	Generate second random int	Seed=updated	Random int between 1-10	1
4	End of execution	Seed=updated	N/A	Outputs: 2, 1

💡 Random numbers generated based on seed 42, ensuring reproducibility.

Status Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	Final
seed	None	42	internal updated	internal updated	internal updated
random_number_1	None	None	2	2	2
random_number_2	None	None	None	1	1

Key Moments - 3 Insights

Why do the random numbers stay the same every time we run the code?

Because setting the seed (Step 1) fixes the starting point of the random number generator, so the sequence of numbers generated (Steps 2 and 3) is always the same.

What happens if we don't set a seed before generating random numbers?

Without setting a seed, the random number generator starts from a different state each time, so the numbers will differ on each run (not shown in table but implied).

Does the seed variable itself change after generating numbers?

The seed is used to initialize the generator; internally the generator state updates after each number, but the original seed value remains conceptually fixed (see 'internal updated' in variable_tracker).

Visual Quiz - 3 Questions

Look at the execution table, what is the first random number generated at Step 2?

A 2

B 1

C 42

D None

💡 Hint

Check the 'Random Number Generated' and 'Output' columns at Step 2 in the execution_table.

At which step does the seed get set to 42?

A Step 2

B Step 1

C Step 3

D Step 4

💡 Hint

Look at the 'Action' column in the execution_table to find when the seed is set.

If we change the seed to 100, what will happen to the output numbers?

A They will be the same as with seed 42

B No numbers will be generated

C They will be different from seed 42

D The seed will reset to 42 automatically

💡 Hint

Refer to the concept_flow where changing the seed leads to different results.

Concept Snapshot

Random seed management:
- Use random.seed(value) to fix randomness
- Fixing seed makes outputs reproducible
- Same seed = same random sequence
- Different seed = different sequence
- Useful for debugging and consistent experiments

Full Transcript

Random seed management means setting a starting point for random number generation so results repeat every time. We set the seed using random.seed(42). Then, when we generate random numbers, they come out the same on every run. This helps in machine learning experiments to get consistent results. The seed initializes the random generator's state. Each call to generate a random number updates this state internally. If we change the seed, the sequence of random numbers changes. Without setting a seed, the numbers vary each run. This trace showed setting seed 42 and generating two random numbers: 2 and 1, always the same on every run.