Product updates, news, tutorials, integrations, and deep dives.
Tutorial
What data scientists need to know about DevOps
A philosophical and practical guide to using continuous integration (via GitHub Actions) to build an automatic model training system.
Packaging data and machine learning models for sharing
A virtual poster for SciPy 2020 about sharing versioned datasets and ML models with DVC.
AITA for making this? A public dataset of Reddit posts about moral dilemmas
Releasing an open natural language dataset based on r/AmItheAsshole.
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?
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.
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.
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.
