Vertex AI Pipelines and ML Artifacts Guide

Vertex AI Pipelines help turn machine learning steps into repeatable workflows. Instead of manually running data preparation, training, evaluation, and deployment, a pipeline defines the steps and orchestrates them.

This guide explains the basics of pipelines and ML artifacts for beginners.

Quick Answer

Use Vertex AI Pipelines when your ML workflow needs to be repeatable, trackable, and easier to productionize. A pipeline is made of components, and each component can produce artifacts such as datasets, metrics, models, containers, and metadata.

Key Takeaways

• Pipelines help automate and standardize ML workflows.
• A pipeline is made of modular components.
• Components can pass outputs to later steps.
• Artifacts help track what happened during a run.
• Metadata and lineage help explain why a model performed a certain way.
• Pipeline definitions and training code should be version controlled.

What Is A Pipeline?

A machine learning pipeline is a repeatable workflow. It may include:

• data extraction,
• preprocessing,
• dataset creation,
• model training,
• evaluation,
• model registration,
• endpoint creation,
• deployment,
• monitoring setup.

The pipeline makes the process easier to rerun and compare.

Pipeline Components

A component is one step in the workflow.

Examples:

• create dataset,
• preprocess data,
• train model,
• evaluate model,
• upload model,
• create endpoint,
• deploy model.

Components are useful because they make the workflow modular. A team can update one part without rewriting the whole process.

Why Pipelines Matter

Pipelines help answer important questions:

• Which run produced the best model?
• Which dataset was used?
• Which hyperparameters were used?
• Which code version trained the model?
• Which model is currently deployed?
• Why did one run perform better than another?

These questions matter when ML becomes part of real operations.

ML Artifacts

Artifacts are outputs from ML workflow steps.

Common artifacts:

Artifacts make the workflow traceable.

Artifact Lineage

Lineage explains how an artifact was created.

For a model, lineage may include:

• training data,
• validation data,
• preprocessing code,
• training code,
• hyperparameters,
• container image,
• evaluation metrics,
• pipeline run,
• downstream deployed model.

This helps teams investigate performance changes and reproduce results.

Practical Pipeline Workflow

1. Define the ML workflow.
2. Break it into components.
3. Store code in version control.
4. Store containers in a secure registry.
5. Compile the pipeline.
6. Run the pipeline.
7. Review metrics and artifacts.
8. Register or deploy the model only if it passes checks.

Best Practices

• Use Git for pipeline definitions and training code.
• Keep datasets and artifacts in approved storage.
• Use clear names for pipeline runs.
• Record metrics in a consistent format.
• Keep model artifacts separate from source code.
• Track container images used for training and serving.
• Review pipeline outputs before deployment.

Common Mistakes

• running everything manually from notebooks
• not saving metrics and artifacts
• not versioning pipeline code
• mixing data, code, and model outputs together
• deploying without an evaluation gate
• losing track of which model version is in production

Bottom Line

Vertex AI Pipelines help make ML workflows repeatable and explainable. Learn pipelines as a production discipline: define components, track artifacts, preserve lineage, and use version control so model development can be trusted over time.