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Building ML Pipelines with MLFlow

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A practical guide for you to log projects with MLFlow

Introduction

MLflow is a very helpful tool for data scientists. It’s an open-source platform designed to manage the entire ML lifecycle, from experimentation to deployment. If you already use it, you know what I am talking about. If not, you will see that it is your trusty sidekick for MLOps.

Why should you care about MLflow?

Well, it brings reproducibility, streamlines collaboration, and simplifies deployment. This article will guide you through building a complete ML pipeline using MLflow, covering data preprocessing and model training.

Ready to dive in?

MLFlow helps ML lifecycle | Image generated by AI. MEta, 2025. https://meta.ai

Understanding MLflow Components

MLflow isn’t just one tool; it’s a collection of components working together:

  • MLflow Tracking: Keeps track of your experiments, logging parameters, metrics, and artifacts. It’s like a detailed lab notebook for your ML projects.
  • MLflow Projects: Packages your code, ensuring reproducibility across different environments. Say goodbye to “it works on my machine” issues!
  • MLflow Models: Packages your trained ML models with all the necessary metadata for deployment. Think of it as a standardized way…

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Data Science Collective
Data Science Collective

Published in Data Science Collective

Advice, insights, and ideas from the Medium data science community

Gustavo R Santos
Gustavo R Santos

Written by Gustavo R Santos

Data Scientist | I solve business challenges through the power of data. | Visit my site: https://gustavorsantos.me

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