Why it matters

Pipelines shape reusable ML workflows. Understanding enables production Spark ML.

Advertisement

The architecture

Stages: transformers (transform data) + estimators (fit model).

fit(): trains pipeline. transform(): applies.

Pipeline lifecyclePipeline stagesTransformer + Estimator chainfit()trainstransform()appliesSame pipeline used for train + inference; ensures consistency
ML pipeline flow.
Advertisement

How it works end to end

Persistence: save + load pipelines. Version + deploy.

Params: tune via ParamGridBuilder + CrossValidator.

Custom stages: extend Transformer/Estimator for custom logic.