ML pipeline overview

Pipeline Structure

ML pipelines are defined as directed acyclic graphs (DAGs) composed of deterministic stages:

1. Data Validation Ensures schema consistency, detects missing values, and validates statistical properties.

2. Feature Engineering Applies version-controlled transformations to raw data.

3. Model Training Executes training jobs with fixed configurations and random seeds.

4. Model Evaluation Computes performance, fairness, and robustness metrics.

5. Model Registration Registers models that meet acceptance criteria.

6. Deployment Trigger Initiates automated deployment workflows.

Idempotent execution

Automatic retries

Artifact versioning

Failure isolation