Experiments

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

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.

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

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.

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.

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

Using DVC, you'll be able to track the changes that give you an ideal model.

In DVC 2.0, we’ve introduced a new feature set aimed at simplifying the versioning of lightweight ML experiments. In this post, we’ll dive into how exactly these new experiments work.