Tutorial

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.

DVC 1.0 optimized data synchronization to and from remote storage. Here's how we did it.

Catch our monthly updates- featuring new video docs and talks, new jobs at DVC, and must-read contributions from the community about MLOps, data science with R, and ML in production.

This month, hear about our international talks, new video docs on our YouTube channel, and the best tutorials from our community.

Use your own GPUs with GitHub Actions & GitLab for continuous machine learning.

A video tutorial about using continuous integration in data science and machine learning projects. This tutorial shows how to use GitHub Actions and Continuous Machine Learning (CML) to create your own automated model training and evaluation system.

A philosophical and practical guide to using continuous integration (via GitHub Actions) to build an automatic model training system.