Data Version Control Blog

Insights and updates from the DVC team. Explore best practices in data versioning, machine learning workflows, and model management. Stay informed with our latest news, tutorials, and community highlights.
April ’19 DVC❤️Heartbeat
DVC creator Dmitry Petrov is giving a talk on PyCon 2019 🎤, new DVC logo design, new Discord discussions, interesting reads that caught our eye, and everything along the way.
  • Svetlana Grinchenko
  • Apr 18, 20198 min read
March ’19 DVC❤️Heartbeat
The very first issue of the DVC Heartbeat! News, links, Discord discussions from the community.
  • Svetlana Grinchenko
  • Mar 05, 20193 min read
ML best practices in PyTorch dev conf 2018
In the Machine Learning (ML) field tools and techniques for best practices are just starting to be developed.
  • Dmitry Petrov
  • Oct 18, 20184 min read
Best practices of orchestrating Python and R code in ML projects
What is the best way to integrate R and Python languages in one data science project? What are the best practices?
  • Marija Ilić
  • Sep 26, 20176 min read
ML Model Ensembling with Fast Iterations
Here we'll talk about tools that help tackling common technical challenges of building pipelines for the ensemble learning.
  • George Vyshnya
  • Aug 23, 20178 min read
Data Version Control in Analytics DevOps Paradigm
Why DevOps matters in data science, what specific challenges data scientists face in the day to day work, and how do we setup a better environment for the team.
  • George Vyshnya
  • Jul 27, 20174 min read
R code and reproducible model development with DVC
There are a lot of example on how to use Data Version Control (DVC) with a Python project. In this document I would like to see how it can be used with a project in R.
  • Marija Ilić
  • Jul 24, 20179 min read
How Data Scientists Can Improve Their Productivity
Data science and machine learning are iterative processes. It is never possible to successfully complete a data science project in a single pass.
  • Dmitry Petrov
  • May 15, 20174 min read