ML Pipeline Guide

Overview

Machine learning research increasingly demands reproducible, end-to-end pipelines that go beyond a single training script. A research ML pipeline encompasses data ingestion, feature engineering, model training, evaluation, experiment tracking, and artifact management. Without a structured pipeline, research results become difficult to reproduce, ablation studies become error-prone, and collaborators cannot build on prior work.

This guide covers the practical tools and patterns for building ML pipelines in an academic research context. The focus is on reproducibility, experiment tracking, and the transition from notebook prototyping to structured experiments. The patterns use MLflow, DVC, and standard Python tooling -- chosen because they are open source, widely adopted in published research, and require minimal infrastructure.

Unlike industry MLOps guides that emphasize deployment at scale, this guide prioritizes the research workflow: running many experiments, tracking what changed between runs, and producing results that reviewers can verify.

Pipeline Architecture

A research ML pipeline typically has five stages: