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Transcript#
This transcript was generated automatically and may contain errors.
Let's deploy a Streamlit web app together. I built the Streamlit web app as a UI to surface my machine learning model, so now let's deploy it so our non-technical stakeholders can use this model super easily.
I like using Connect Cloud because of how simple it is. My background is in data science, and I want to make deployment as easy as possible, and Connect Cloud is doing exactly that.
My background is in data science, and I want to make deployment as easy as possible, and Connect Cloud is doing exactly that.
Deploying with Connect Cloud
All you have to do is log in using your existing GitHub account, select the repository where you built your application, make sure you've uploaded your requirements.txt file in the repository so Connect Cloud knows exactly what packages to install when running your app, then you click deploy. It only takes a few minutes to spin up, and then your application is live and ready for use.
Connect Cloud can also double as your personal portfolio, so make sure you're uploading all of your projects here. Leave any questions in the comments below, and follow along for more data science content. Bye!
Tags:Rstudio,Data Science,Machine Learning,Python,Stats,Tidyverse,Data Visualization,Data Viz,Ggplot,Technology,Coding,Connect,Server Pro,Shiny,RMarkdown,Package Manager,CRAN,Interoperability,Serious Data Science,Dplyr,Forcats,Ggplot2,Tibble,Readr,Stringr,Tidyr,Purrr,Github,Data Wrangling,Tidy Data,Odbc,Rayshader,Plumber,Blogdown,Gt,Lazy Evaluation,Tidymodels,Statistics,Debugging,Programming Education,Rstats,Open Source,OSS,Reticulate
