Why reproducibility builds trust in ML in MLOps - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to save the model with reproducible results.

MLOps

model.save('model_[1].h5')

Drag options to blanks, or click blank then click option'

Av1

Btemp

Cfinal

Dtest

Attempts:

3 left

💡 Hint

Common Mistakes

Using generic names like 'temp' or 'test' which don't track versions.

Using a version like 'v1' helps keep track of reproducible model builds.

2fill in blank

medium

Complete the code to set a fixed random seed for reproducibility.

MLOps

import numpy as np
np.random.seed([1])

Drag options to blanks, or click blank then click option'

ANone

Brandom

C42

D-1

Attempts:

3 left

💡 Hint

Common Mistakes

Using None or 'random' which do not fix the seed.

Setting a fixed seed like 42 ensures the random numbers are the same every run.

3fill in blank

hard

Fix the error in the code to log parameters for reproducibility.

MLOps

mlflow.log_param('learning_rate', [1])

Drag options to blanks, or click blank then click option'

A0.01

B'0.01'

Clearning_rate

DNone

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the learning rate as a string or variable name instead of a number.

Logging the parameter as a number (0.01) is correct for reproducibility tracking.

4fill in blank

hard

Fill both blanks to create a reproducible data split.

MLOps

train_data, test_data = train_test_split(data, test_size=[1], random_state=[2])

Drag options to blanks, or click blank then click option'

A0.2

B0.5

C42

DNone

Attempts:

3 left

💡 Hint

Common Mistakes

Using None for random_state or incorrect test sizes.

Using test_size=0.2 and random_state=42 ensures the split is reproducible.

5fill in blank

hard

Fill all three blanks to log model metrics reproducibly.

MLOps

mlflow.log_metric('[1]', [2], step=[3])

Drag options to blanks, or click blank then click option'

Aaccuracy

B0.95

C1

Dloss

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing metric names and values or missing the step parameter.

Logging 'accuracy' with value 0.95 at step 1 helps track reproducible metrics.

Practice

1. What does reproducibility in machine learning primarily ensure?

easy

A. The same steps produce the same results every time

B. The model trains faster on new data

C. The model uses less memory during training

D. The model automatically improves accuracy over time

Solution

1. Step 1: Understand reproducibility meaning

   Reproducibility means repeating the same process and getting the same results.

2. Step 2: Identify what reproducibility guarantees

   It guarantees consistent results, not speed, memory, or automatic improvement.

3. Final Answer:

   The same steps produce the same results every time -> Option A

4. Quick Check:

   Reproducibility = consistent results [OK]

Hint: Reproducibility means repeat and get same results [OK]

Common Mistakes:

• Confusing reproducibility with performance improvements
• Thinking reproducibility means automatic model updates
• Assuming reproducibility reduces resource use

2. Which practice helps ensure reproducibility in ML experiments?

easy

A. Skipping data preprocessing steps

B. Increasing batch size randomly

C. Using random seeds to fix randomness

D. Changing model architecture each run

Solution

1. Step 1: Identify reproducibility techniques

   Fixing randomness with seeds ensures the same random choices each run.

2. Step 2: Evaluate options for reproducibility

   Changing batch size, model, or skipping steps breaks reproducibility.

3. Final Answer:

   Using random seeds to fix randomness -> Option C

4. Quick Check:

   Random seeds fix randomness [OK]

Hint: Fix randomness with seeds for reproducibility [OK]

Common Mistakes:

• Thinking changing model each run helps reproducibility
• Ignoring the role of data preprocessing
• Assuming random batch sizes improve reproducibility

3. Given this Python snippet for setting a random seed:

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

What will be the output every time you run it?

medium

A. The number 2 every time

B. A different random number between 1 and 10 each run

C. The number 10 every time

D. An error because seed is not set correctly

Solution

1. Step 1: Understand random.seed(42)

   Setting seed fixes the random number sequence to be repeatable.

2. Step 2: Check random.randint(1, 10) with seed 42

   With seed 42, random.randint(1, 10) returns 2 every time.

3. Final Answer:

   The number 2 every time -> Option A

4. Quick Check:

   Seed 42 fixes output to 2 [OK]

Hint: Seed fixes random output to same number [OK]

Common Mistakes:

• Expecting different numbers each run despite seed
• Assuming seed causes errors
• Guessing max or min number instead of actual output

4. You run an ML experiment but get different results each time. Which fix will improve reproducibility?

medium

A. Remove version control from code

B. Disable containerization tools

C. Use different datasets each run

D. Set fixed random seeds in all libraries

Solution

1. Step 1: Identify cause of varying results

   Randomness without fixed seeds causes different results each run.

2. Step 2: Choose fix to ensure reproducibility

   Setting fixed seeds in all libraries ensures consistent randomness and results.

3. Final Answer:

   Set fixed random seeds in all libraries -> Option D

4. Quick Check:

   Fixed seeds improve reproducibility [OK]

Hint: Fix randomness by setting seeds everywhere [OK]

Common Mistakes:

• Removing version control thinking it helps
• Changing datasets each run breaks reproducibility
• Disabling containers reduces environment consistency

5. Which combination of practices best builds trust through reproducibility in ML?

hard

A. Training on different data splits without logging

B. Using random seeds, version control, and containerization

C. Changing hyperparameters randomly each run

D. Ignoring environment setup and dependencies

Solution

1. Step 1: Identify key reproducibility practices

   Random seeds fix randomness, version control tracks code, containers fix environment.

2. Step 2: Evaluate options for trust-building

   Only Using random seeds, version control, and containerization combines all these to ensure consistent, repeatable results.

3. Final Answer:

   Using random seeds, version control, and containerization -> Option B

4. Quick Check:

   Seeds + version control + containers = trust [OK]

Hint: Combine seeds, version control, containers for trust [OK]

Common Mistakes:

• Randomly changing hyperparameters breaks reproducibility
• Skipping logs loses experiment traceability
• Ignoring environment causes inconsistent runs