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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.
Elle O'Brien
Jul 16, 2020 • 9 min read
Packaging data and machine learning models for sharing
A virtual poster for SciPy 2020 about sharing versioned datasets and ML models with DVC.
Elle O'Brien
Jun 26, 2020 • 5 min read
AITA for making this? A public dataset of Reddit posts about moral dilemmas
Releasing an open natural language dataset based on r/AmItheAsshole.
Elle O'Brien
Feb 17, 2020 • 8 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, 2017 • 6 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, 2017 • 8 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, 2017 • 9 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, 2017 • 4 min read
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