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    Data Version Control in Real Life

    We write about machine learning workflow. From data versioning and processing to model productionization. We share our news, findings, interesting reads, community takeaways.
    March ’19 DVC❤️Heartbeat
    The very first issue of the DVC Heartbeat! News, links, Discord discussions from the community.
    • Svetlana Grinchenko
    • Mar 05, 20195 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, 201710 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, 201713 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, 20176 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, 201716 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, 20177 min read