Principle:Kubeflow Kubeflow Build Pipeline
| Knowledge Sources | |
|---|---|
| Domains | MLOps, Pipeline Orchestration, Reproducibility |
| Last Updated | 2026-02-13 00:00 GMT |
Overview
Build Pipeline is the principle of encoding machine learning workflows as directed acyclic graphs (DAGs) of reusable, containerized components to achieve reproducibility, auditability, and automation.
Description
Once experimental prototyping yields a viable approach, the next step in the ML lifecycle is to formalize the workflow into a reproducible pipeline. A pipeline defines the sequence of data processing, feature engineering, model training, evaluation, and artifact storage steps as a DAG where each node is an isolated, containerized component with explicit inputs and outputs.
This principle addresses several fundamental challenges in ML engineering: ensuring that experiments can be exactly reproduced, enabling collaboration by making workflows shareable and version-controlled, automating repetitive execution cycles, and creating an auditable lineage from raw data to deployed model.
Within the Kubeflow ecosystem, Kubeflow Pipelines (KFP) provides the platform for defining, compiling, and executing these DAGs on Kubernetes. The KFP SDK v2 allows engineers to define components as Python functions or container operations, compose them into pipelines using a decorator-based API, compile the pipeline to an intermediate representation (IR YAML), and submit runs to the KFP backend which orchestrates execution and tracks all artifacts in ML Metadata (MLMD).
Usage
Apply this principle when:
- Prototype code is mature enough to be decomposed into discrete, reusable steps.
- The team needs reproducible execution of multi-step ML workflows across environments.
- Artifact tracking and lineage (data, models, metrics) must be systematically recorded.
- Automated scheduling or triggering of ML workflows is required.
- Multiple team members need to collaborate on and review the same workflow definition.
- Compliance or governance requirements demand auditable ML processes.
Theoretical Basis
Building an ML pipeline follows a structured decomposition process:
Step 1: Component Identification
- Analyze the prototype workflow and identify discrete processing steps.
- Each step should have a single responsibility (e.g., data loading, preprocessing, training, evaluation).
- Define the interface for each step: typed inputs, typed outputs, and configuration parameters.
Step 2: Component Implementation
- Implement each component as an isolated unit with explicit dependencies.
- Components may be lightweight Python functions or heavyweight containerized operations.
- Each component must declare its input and output artifacts with proper typing.
Step 3: Pipeline Composition
- Compose components into a DAG by connecting outputs of upstream components to inputs of downstream components.
- Define pipeline-level parameters that can be overridden at run time.
- Specify conditional logic, loops, or parallel fan-out where needed.
Step 4: Compilation and Validation
- Compile the pipeline definition into a portable intermediate representation.
- Validate that all component interfaces are compatible and all required inputs are satisfied.
- Review the compiled DAG for correctness before submission.
Step 5: Execution and Tracking
- Submit the compiled pipeline to the orchestration backend.
- The backend schedules each component as a Kubernetes pod, managing dependencies and retries.
- All inputs, outputs, parameters, and metrics are recorded in the metadata store for lineage tracking.
The key invariant is that a compiled pipeline definition, given the same inputs and component versions, must produce the same outputs. This reproducibility guarantee is what distinguishes a pipeline from ad-hoc script execution.