Component specification

As explained in Machine Learning Platform Engineering

A component specification is a declarative definition of a pipeline component's inputs, outputs, container image, command, and arguments.

How Machine Learning Platform Engineering covers it

How deep it goes

Explains YAML fields, container commands, argument bindings, and artifact typing, while deferring broader pipeline orchestration.

Examples

Uses read-data, feature-retrieval, inference, and write-data components backed by Docker images and YAML definitions.

What it assumes

Assumes Python component code and Docker packaging, while demonstrating the specification interface from its YAML fields.

What you can do after

Builds a containerized component and applies its specification in a Kubeflow pipeline.

How it connects

Connects Docker, YAML, Kubeflow pipelines, Dataset artifacts, MinIO, Feast, and MLflow.

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Related concepts

• pipeline componentconfigures · A pipeline component is a discrete, executable stage in a data or machine-learning workflow that performs an operation and may consume or produce data artifacts.
• AI agentuses · An AI agent is a computational system that uses artificial intelligence to interpret information, select actions, and pursue goals through interaction with an environment.
• design documentpart of · A design document is written material describing an intended software design, including its structure, technical decisions, rationale, constraints, or implementation considerations.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning Platform Engineering. The section links point to the parts of the book it draws on.