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. 

Appsilon created a dashboard in collaboration the International Institute for Applied Systems Analysis to deliver information about COVID-19 to policymakers. We implemented the solution with Python and Svelte.

R Shiny has historically been used for data-driven applications and as a rapid development tool, but it's a highly adaptable technology. Is it possible to build a functioning video game in R Shiny? Let's find out.

Our favorite RStudio shortcuts, tips, and tricks to increase productivity while working with the RStudio IDE. We cover keyboard shortcuts, custom code snippets, addins, and more.

Dynamic Shiny Modules can destabilize the Shiny environment and cause its reactive graph to be rendered multiple times. Here is how to safely remove deleted module leftovers and make sure that your Shiny graph observers are rendered properly.

Distributed data science teams bring unique challenges. Managers may be looking for new tools. In this article we’ll explain how RStudio Connect helps organizations to properly organize teams and overcome the typical inefficiencies of remote work.

Introduction to object detection and image classification featuring the YOLO algorithm and its Darknet implementation

In this article, we focus on the technical aspects of the machine learning solution we implemented for the xView2 competition. We used PyTorch to build our models for satellite image analysis and fast.ai to develop their critical parts.

Our submission to the Pandemic Response Hackathon (CoronaRank) is inspired by Google’s PageRank and utilizes geolocation data in the Apache Parquet format from Veraset for effective exposure risk assessment using R and Markov Chain modeling.

This is the first of two blog posts about our recent participation in the Pandemic Response Hackathon. Our project (CoronaRank) was one of only 5 projects out of 230 submissions chosen to present at the closing ceremony.

Appsilon’s open source shiny.info package just received a major update and is now available on CRAN. A great solution for development and testing, shiny.info allows for displaying diagnostic information from inside Shiny apps.

Artificial Intelligence, Machine Learning, and Deep Learning models have demonstrated significant power to grow and improve businesses. This article explains how to properly approach developing an AI model.

As part of our D4G Initiative and with the support of a Google grant, Appsilon Data Science will be contributing to to the work of biodiversity conservationists at the National Parks Agency in Gabon in collaboration with the University of Stirling.

Can you build Shiny apps without any R knowledge? This R Shiny Dashboard Tutorial shows you my experience with learning R creating R Shiny dashboards in 2 days.

Learn how to find and use satellite image datasets for data science and machine learning projects with this beginner-friendly guide.

Have you considered adding AI/ML to your organization's operations? AI models can significantly reduce costs when they are implemented properly, but can end up in "purgatory" with the wrong approach. Here's how to properly implement AI models.

We'll show you how to import the proper libraries, set up a dashboard, add UI components, and more!

Appsilon engineers Paweł and Pedro share their perspective on rstudio::conf 2020, especially news and updates about R packages...

RStudio::conf 2020 is a wrap!

We recently took part in Hakuna-ma Data, a competition organised by DrivenData in partnership with Microsoft’s AI for Earth, which asked participants to build an algorithm for wildlife detection that would generalise well across time and locations...

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 is an RStudio Certified Partner offering R Shiny dashboards, Managed Services, and RStudio product implementations.

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?

Why stick to Excel and risk poorly maintained data or serious miscalculations? Discover efficiency gains brought by R Shiny and Python Dash

Building Deep Learning vision models satellite imagery to assist in identifying and labeling property damage from natural disasters.

Learn to create R apps with this R Shiny Dashboard Tutorial! We'll go from simple R Shiny to Bootstrap, Semantic UI, and then cover the custom version.

The city of Poznań wanted to know why it didn't rate highly with tourists. So they gave loads of data to a gathering of developers to gain some insight. TL;DR: we learned a lot from the hackathon & won the thing. R Shiny gave us a slight edge. :)

The algorithms we build affect lives. We have an ethical obligation not to teach our machines to be prejudiced. Here are some lessons and examples that will help you achieve this in your machine learning model assembly and use.

We need to build a culture where AI is built with a focus on the entire outcome, not just metrics or profit.

We live in a competitive time, filled with regional and global challenges, both physical and digital, and yet we are empowered with access to ever-growing data sets and tools at our fingertips: cyber-security, NewSpace, asymmetry of information