Experiments

From Jupyter Notebook to DVC pipeline for reproducible ML experiments

In this guide we will take a Jupyter Notebook and use Papermill to turn it into a simple, one-stage DVC pipeline.

Rob de Wit

October 24, 2022

13 minutes read

Preventing Stale Models in Production

We're going to look at how you can prevent stale models from remaining in production when the data starts to differ from the training data.

Milecia McGregor

March 31, 2022

7 minutes read

Running Collaborative Experiments

Sharing experiments with teammates can help you build models more efficiently.

Milecia McGregor

December 13, 2021

5 minutes read

Don’t Just Track Your ML Experiments, Version Them

ML experiment versioning brings together the benefits of traditional code versioning and modern day experiment tracking, super charging your ability to reproduce and iterate on your work.

Dave Berenbaum

December 7, 2021

6 minutes read

Adding Data to Build a More Generic Model

You can easily make changes to your dataset using DVC to handle data versioning. This will let you extend your models to handle more generic data.

Milecia McGregor

October 5, 2021

7 minutes read

Using Experiments for Transfer Learning

You can work with pretrained models and fine-tune them with DVC experiments.

Milecia McGregor

August 24, 2021

11 minutes read