Intermediate · Data

Data Pipeline

Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.

TL;DR. An automated workflow that ingests, transforms, and delivers data to models.

Technical Definition

An automated workflow that ingests, transforms, and delivers data to models.

How it works

A typical ML pipeline includes ingestion, validation, cleaning, feature engineering, storage, and serving. Tools like Airflow, dbt, Spark, and Kafka orchestrate these stages. Reliable pipelines are often the difference between a research prototype and a production ML system.

Related Concepts

• Feature Engineering — The process of creating, selecting, and transforming input variables to improve a machine learning model's performance.
• Dataset — An organized collection of examples used to train, validate, or test a model.
• Data Preprocessing — Transforming raw data into a format suitable for model training.