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

Process Flow - Pipeline versioning and reproducibility

Define pipeline steps

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Assign version to pipeline

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Run pipeline with version

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Save pipeline artifacts and metadata

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Re-run pipeline using saved version

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Compare outputs for reproducibility

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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.