Scaling Shiny Apps – from idea to impact

WHEN

21 August 2025
11am AEST (Sydney) | 1pm NZST (Auckland)

 

WHERE

Online webinar

Webinar recording

Taking your Shiny app from an analytical idea or research output to a fully operational, well-designed and robust piece of software accessed by many users can be highly frustrating. It is often where things fall apart, despite skill and enthusiasm. There is a big step involved in getting from a prototype or first version to a final, deployable 'production' version. This involves making sure that critical success factors such as user experience, authentication, hosting and data permissions are addressed, as end users will have high expectations and may use the application in unexpected ways!

In this webinar, we will discuss pain points and practical solutions involved with scaling a Shiny app into production.  We will touch on topics like data access, code modularisation, design considerations, performance profiling and tuning, and provide examples from our own experiences in building production grade Shiny apps across many domains and levels of complexity.

Speakers

Dr Uli Muellner

Managing Director | Epi

A highly experienced tech leader Uli has 30+ years’ experience in crafting people-first IT solutions. Equipped with a PhD in online learning and knowledge management, and a background in computer science, his keen sense for quality execution is at the core of what we do. Uli is passionate about helping our clients get the most out of open-source data science tooling and has been leading our Posit partnership since its beginning in 2020. 

Nick Snellgrove

Tech Lead | Epi

Nick leads Epi’s dashboard engineering team, guiding standards and mentoring developers to ship fast, reliable data applications. Beyond his well‑known R and Shiny expertise, he has hands‑on experience with Python workflows, relational databases, Docker and AWS, enabling him to deploy interactive data tools that scale securely in the cloud. He shares best practice through Epi’s annual Shiny Masterclass.

Kai Lewis

Research Software Lead | Epi

Kai bridges applied science and software engineering, turning research prototypes into robust, cloud‑native tools. He has led developer chapters and built full‑stack data applications using Python, FastAPI, Next.js/TypeScript and R Shiny. Comfortable with Azure, Kubernetes and CI/CD automation, he champions reproducible pipelines and collaborative engineering standards that let scientists ship insights sooner.

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