Table of Contents

DVC Pipelines for MLOps: Build a Reproducible ML Pipeline

Introduction

Project Setup

Understanding the Pipeline Architecture

Stage 1: Dataset Preprocessing

Stage 2: Model Training and Versioning with DVC

Stage 3: Model Evaluation and Metrics Tracking with DVC

Running and Reproducing the Pipeline

Visualizing and Inspecting Pipelines

Extending the Pipeline (Optional Enhancements)

Summary

In this lesson you will learn how to build a fully reproducible machine learning pipeline using DVC, starting from raw data and moving through preprocessing, model training, and evaluation, all connected inside a clean and automated workflow that you can run with a single command.

This lesson is the last in a 2-part series on Data and Model Versioning with DVC :

DVC for MLOps: Versioning Your Data and Models the Right Way

DVC Pipelines for MLOps: Build a Reproducible ML Pipeline (this tutorial)

To learn how to design, run, and reproduce a multi-stage pipeline using DVC’s powerful workflow…