---
title: "How do you build a reproducible ML pipeline with DVC and AWS S3?"
description: "Build a reproducible spam-detection pipeline with DVC stages, versioned artifacts, and AWS S3 remote storage."
canonical_url: "https://fanout.sh/ai/lessons/ml-pipeline-dvc-aws-s3"
md_url: "https://fanout.sh/ai/lessons/ml-pipeline-dvc-aws-s3.md"
access: "public"
---

# How do you build a reproducible ML pipeline with DVC and AWS S3?

Build a reproducible spam-detection pipeline with DVC stages, versioned artifacts, and AWS S3 remote storage.

## Public overview

End-to-end NLP Spam Detection pipeline with MLOps best practices.

Pipeline Stages: Data Ingestion → Data Validation → Pre-processing → Model Training & Evaluation

Key Properties: Modularity: Each stage is independent — robust and maintainable Data Versioning: DVC tracks data/model versions with code-level rigor Cloud Integration: AWS S3 as DVC Remote for heavy datasets and models Reproducibility: Defined via dvc.yaml for automatic pipeline execution

Infrastructure: IAM User & S3 for secure storage, dvc stage add with dependencies (-d) and outputs (-o).

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