R Shiny is a great framework that is easy-to-use and helps data scientists quickly re-use their code for basic interactive dashboards. Have you ever wondered how it can be used in real life and how much you can do with it?
We have accumulated a few interesting findings in a past project where we built a complicated Shiny dashboard and deployed it on Kubernetes. This dashboard displayed analysis and visualization based on a huge amount of proprietary data in Snowflake. We went on experimenting in many areas to push for a pleasant user perception and experience beyond a functional dashboard. The goal to productize the dashboard for at least hundreds of concurrent users to connect also drove us to optimize the performance such as resource utilization as much as possible.
In this talk, we’d like to share a few lessons we learned during this journey, and hopefully they can help you avoid some pitfalls in the future.
Speaker Bio
Wendy (Wanting) Wang (LinkedIn) is a senior data scientist with IBM Expert Labs, Data & AI. After graduating from Columbia University with a master’s degree in Quantitative Methods in the Social Sciences, she joined IBM and worked as a client-facing data scientist to tackle challenging business problems from various industries, covering use cases in data analytics, machine learning, deep learning, MLOps, and trustworthy AI.
Agenda
- 10:45 — Doors open
- 11:00 — Welcome
- 11:02 — IBM Community announcements
- 11:08 — Main talk + Q&A
- 11:50 — Stick around & mingle
About our virtual meetupsOur meetups are a chance for the IBM Data Science Community team to hear from our members. If you have feedback or ideas regarding the community, please feel free to show up a little bit early and talk to us. If you have announcements you’d like to share in front of the attendees, let us know, and we’ll try to accommodate you at the beginning of the session.
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