Pipeline versioning and reproducibility in MLOps - Step-by-Step Execution

Process Flow - Pipeline versioning and reproducibility

Define pipeline steps

↓

Assign version to pipeline

↓

Run pipeline with version

↓

Save pipeline artifacts and metadata

↓

Re-run pipeline using saved version

↓

Compare outputs for reproducibility

↓

End

This flow shows how a pipeline is versioned, run, saved, and re-run to ensure the same results can be reproduced.

Execution Sample

MLOps

pipeline = Pipeline(steps=[step1, step2])
pipeline.version = 'v1.0'
output1 = pipeline.run()
pipeline.save('pipeline_v1.0')
output2 = pipeline.load('pipeline_v1.0').run()

This code defines a pipeline, assigns a version, runs it, saves it, then reloads and reruns to check reproducibility.

Process Table

Step	Action	Pipeline Version	Output	Notes
1	Define pipeline steps	None	None	Pipeline steps set but no version yet
2	Assign version 'v1.0'	v1.0	None	Pipeline version set for tracking
3	Run pipeline first time	v1.0	Output A	Pipeline executes producing Output A
4	Save pipeline and artifacts	v1.0	Output A	Pipeline state and output saved
5	Load saved pipeline version	v1.0	None	Pipeline loaded from saved version
6	Run pipeline second time	v1.0	Output A	Pipeline re-executed to check reproducibility
7	Compare outputs	v1.0	Output A == Output A	Outputs match, confirming reproducibility
8	End	v1.0	Reproducible	Pipeline versioning and reproducibility confirmed

💡 Pipeline outputs match on re-run, confirming reproducibility with version 'v1.0'

Status Tracker

Variable	Start	After Step 2	After Step 3	After Step 4	After Step 5	After Step 6	Final
pipeline.version	None	v1.0	v1.0	v1.0	v1.0	v1.0	v1.0
output1	None	None	Output A	Output A	Output A	Output A	Output A
output2	None	None	None	None	None	Output A	Output A

Key Moments - 3 Insights

Why do we assign a version to the pipeline before running it?

Assigning a version (see Step 2 in execution_table) helps track which pipeline code and configuration produced which outputs, enabling reproducibility.

What ensures that the pipeline output is reproducible?

The outputs from the first run (Step 3) and the re-run after loading the saved pipeline (Step 6) must match, as shown in Step 7.

Why do we save the pipeline and its artifacts after the first run?

Saving (Step 4) preserves the exact pipeline state and outputs so it can be reloaded and rerun later to verify reproducibility.

Visual Quiz - 3 Questions

Look at the execution_table, what is the pipeline version assigned at Step 2?

A None

B v1.0

C v2.0

D v0.1

💡 Hint

Check the 'Pipeline Version' column at Step 2 in the execution_table.

At which step does the pipeline produce its first output?

A Step 2

B Step 4

C Step 3

D Step 5

💡 Hint

Look at the 'Output' column in execution_table to find when output first appears.

If the outputs at Step 3 and Step 6 did not match, what would that indicate?

A Pipeline is not reproducible

B Pipeline version was not assigned

C Pipeline is reproducible

D Pipeline was not saved

💡 Hint

Refer to Step 7 in execution_table where output comparison confirms reproducibility.

Concept Snapshot

Pipeline Versioning & Reproducibility:
- Assign a version to your pipeline before running.
- Run pipeline and save outputs/artifacts.
- Reload and rerun the same version.
- Compare outputs to confirm reproducibility.
- Versioning tracks changes and ensures consistent results.

Full Transcript

Pipeline versioning and reproducibility means giving your pipeline a version label before running it. This helps keep track of which code and settings created which results. After running the pipeline, you save its outputs and metadata. Later, you can reload that exact version and run it again. If the outputs match the first run, the pipeline is reproducible. This process helps ensure your machine learning workflows are reliable and consistent over time.

Practice

1. What is the main purpose of pipeline versioning in MLOps?

easy

A. To increase the size of the dataset used

B. To speed up the training process of machine learning models

C. To track changes in workflows and configurations over time

D. To automatically fix bugs in the code

Solution

1. Step 1: Understand pipeline versioning

   Pipeline versioning means keeping track of changes made to the steps and settings in your workflow.

2. Step 2: Identify the main goal

   This helps teams know what changed and when, making it easier to reproduce or fix issues.

3. Final Answer:

   To track changes in workflows and configurations over time -> Option C

4. Quick Check:

   Pipeline versioning = track changes [OK]

Hint: Versioning means tracking changes over time [OK]

Common Mistakes:

• Confusing versioning with speeding up training
• Thinking versioning fixes bugs automatically
• Believing versioning increases dataset size

2. Which of the following is the correct way to fix a random seed in Python for reproducibility in a pipeline?

easy

A. random.seed(42)

B. random.fix_seed(42)

C. seed.random(42)

D. fix.seed(42)

Solution

1. Step 1: Recall Python random seed syntax

   In Python, the random module uses random.seed(value) to fix the seed.

2. Step 2: Check each option

   Only random.seed(42) matches the correct syntax; others are invalid function calls.

3. Final Answer:

   random.seed(42) -> Option A

4. Quick Check:

   Fix seed in Python = random.seed() [OK]

Hint: Use random.seed(value) to fix seed in Python [OK]

Common Mistakes:

• Using incorrect function names like fix_seed or seed.random
• Confusing method order or syntax
• Missing the random module prefix

3. Given this snippet in a pipeline script:

import random
random.seed(10)
print(random.randint(1, 100))
random.seed(10)
print(random.randint(1, 100))

What will be the output?

medium

A. 67 followed by 67

B. 67 followed by a different number

C. Two different random numbers

D. Error due to repeated seed

Solution

1. Step 1: Understand seed effect on random numbers

   Setting the seed to the same value resets the random number generator to the same state.

2. Step 2: Analyze the code output

   Both calls to random.randint(1, 100) after setting seed(10) will produce the same number, 67.

3. Final Answer:

   67 followed by 67 -> Option A

4. Quick Check:

   Same seed = same random output [OK]

Hint: Same seed resets random sequence, repeat outputs [OK]

Common Mistakes:

• Assuming different outputs after resetting seed
• Thinking repeated seed causes error
• Ignoring seed effect on randomness

4. You run a pipeline but get different results each time, even though you fixed the random seed. What is the most likely cause?

medium

A. The random seed was set correctly

B. The pipeline uses non-deterministic operations or external data changes

C. The pipeline versioning is enabled

D. The code has syntax errors

Solution

1. Step 1: Understand reproducibility factors

   Fixing the random seed controls randomness but does not cover external changes or non-deterministic steps.

2. Step 2: Identify cause of varying results

   If results differ despite fixed seed, likely external data or operations like parallelism cause variation.

3. Final Answer:

   The pipeline uses non-deterministic operations or external data changes -> Option B

4. Quick Check:

   Non-determinism breaks reproducibility [OK]

Hint: Check external data and non-deterministic steps [OK]

Common Mistakes:

• Assuming seed fixes all randomness
• Confusing versioning with reproducibility
• Blaming syntax errors for result changes

5. You want to ensure your ML pipeline is fully reproducible across different machines. Which combination is best to achieve this?

hard

A. Only fix random seeds and ignore environment differences

B. Run pipeline without versioning but log outputs manually

C. Use different random seeds each run and update pipeline versions

D. Fix random seeds, use containerized environments, and version pipeline code

Solution

1. Step 1: Identify reproducibility requirements

   Reproducibility needs fixed seeds, consistent environments, and tracking code changes.

2. Step 2: Evaluate options for best practice

   Fix random seeds, use containerized environments, and version pipeline code combines fixing seeds, containerization for environment consistency, and versioning for tracking changes.

3. Final Answer:

   Fix random seeds, use containerized environments, and version pipeline code -> Option D

4. Quick Check:

   Seeds + containers + versioning = reproducibility [OK]

Hint: Combine seeds, containers, and versioning for full reproducibility [OK]

Common Mistakes:

• Ignoring environment differences
• Changing seeds each run
• Skipping pipeline versioning