Machine Learning Experiments with DVC (Hands-On Tutorial!)

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This tutorial is for total beginners to get started using DVC and Git to version data, models, and more. If you're completely new to DVC and want a gentle walkthrough of the basic features, this is for you!

We'll be mirroring the fourth "Getting Started" feature from the official DVC docs about **experiments**, a method to compare model metrics across Git commits, tags, and branches. We'll explain how to:

- Create DVC metrics in a pipeline
- Compare metrics across Git branches
- Visualize differences in metrics with DVC plots

This is the fourth of 4 videos in the DVC Basics series, covering key approaches in GitOps and MLOps.

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I have been enjoying using DVC to improve my projects - Thanks for this!

ndamulelosbg

This makes working on ML projects so fun 😍😍
I love these tutorials ❤️
Big thanks for your efforts 💚

arminarlert

Excellent tutorials! Elle - fantastic job! 👍🏼

VersusDim

It's all i need for me and the company that i work, thx, you are helping a big DS team here.

jairai

I always enjoy your tutorials. Even if you you're not an expert in ML if you got some basics down you can easily sit back on weekend afternoon with some tea and go step by step on Github, and that is always a great way to learn hands on. Very cool

Also love the visualization features

nextrealm_ai

Thank you for sharing these usefull hands-on tutorials about DVC. I wondered how we can compare DVC to MLflow or airflow? DVC performs pipeline orchestration and some sort of experiments tracking. Can we say one wouldn't need MLflow or airflow anymore if he/she uses DVC?

babakhos

I would like to learn more about, who the training routine gets the parameters.

sorenchannel

are you in the development team of DVC

kachrooabhishek

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