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Explore Posit’s video library for R, Python, and data science tutorials. Learn best practices, workflows, and techniques from our experts.
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Video
How to write and deploy a Python web app with Shiny Express and Posit Team
Winston Chang at Posit joined us to share a quicker and simpler way of writing Shiny applications in Python, called Shiny Express.
Video
How to collaborate effectively with other data scientists (version control, project sharing, etc.)
We were joined by Ryan Johnson for a session dedicated to enhancing teamwork.
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How to train, evaluate, and deploy a machine learning workflow with tidymodels & Posit Team
Simon Couch from the tidyverse team at Posit joined us to walk through an end-to-end machine learning workflow with Posit Team.
Video
How to build a model annotation tool with FastAPI, Quarto & Shiny for Python
Gordon Shotwell, Senior Software Engineer at Posit walks through an end-to-end machine learning workflow with Posit Team. This demo will give you a robust pattern for hosting and sharing models on Connect before you deploy them to a customer-facing system.
Video
How to deploy a Shiny application using clinical trial data to Posit Connect
Open source is changing how drug development happens. Clinical teams are making better decisions, working through clinical trials more confidently, and bringing life-changing drugs to market faster. One way that teams are doing this is with Shiny.
Video
How to schedule a Quarto document with custom branding on Posit Connect
We’ll show you how to deploy company branded reports and schedule updates to be emailed out to your business stakeholders every day so you can get time back in your day.
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How to bring modern UI to your Shiny apps
Join Garrett Grolemund at Posit to learn how to theme and brand your own apps.
Data science hangout
Meet other posit::conf(2024) attendees!
Whether you’re just starting your data science journey, a skilled professional, or a data...
Video
How to develop and deploy a machine learning model with Posit
While data scientists are often taught about training a machine learning model, building a reliable MLOps strategy to deploy and maintain that model can be daunting.