Your Guide to Quality Data Science Products

In this video, Appsilon Senior Data Scientist Olga Mierzwa-Sulima explains best practices for data science teams – whether they are working in the office together or fully remote.

Appsilon Data Science and RStudio will be hosting a joint webinar on Tuesday, July 28th at 11AM EST. The theme is "Enabling Remote Data Science Teams."

Appsilon engineer Pedro Silva has been named a Grand Prize Winner in RStudio's 2nd Annual Shiny Contest. He created a fully functional video game using Shiny.

xspliner is an R package that helps explain black box ML models. In this presentation, you will learn what PDP curves and GLMs are and how you can calculate them based on black box models. I'll then show you a specific use-case for xspliner.

In this talk you’ll learn tools and best practices for making clean, reproducible R code in a working environment ready to be shared and productionized. We cover the benefits of version control, plumber, RStudio Connect, and many other tools.

In this presentation we present how to make use of the latest technical advancements in computer vision with an R setup even if they are originally implemented in Python.

Appsilon engineers Krystian Igras, Marcin Dubel, and Jędrzej Świeżewski, PhD will be giving virtual presentations on Friday, June 19th. Learn about xspliner, making production-ready R code, and using R for Machine Learning projects. 

RStudio::conf 2020 is a wrap!

rstudio::conf 2020 is here! Appsilon Data Science is proud to be a co-sponsor of the conference. Here is some information about the presentations we are doing and others that we hope to attend.

"So we sat down with the project manager on the client side, re-ordered the priorities, estimated additional work and were able to start coding on the very same day."

Appsilon Data Science is now an RStudio Full Service Certified Partner. In addition to building enterprise R Shiny Dashboards, we now offer Managed Services and implementation of RStudio products such as RStudio Connect.

Many AI projects languish in pilot purgatory. Filip Stachura points out known risks in deploying and maintaining AI models. The question becomes, do you have the right people in place to respond to potential and likely issues?