Streamlit
From a model saved on your laptop to a dashboard the sales team opens every morning, in a single Python file.
Course Duration: 3h
What You'll Learn
- Grasp Streamlit's top-to-bottom execution model and design against it rather than around it
- Build a scoring form, then a table of at-risk customers with sidebar filters
- Cache data and cache resources correctly, and know which to reach for
- Manage session state, forms and multi-step navigation without race conditions
- Upload a CSV of customers, score it in batch, and offer the results as a download
- Call a model locally, then a remote inference API with timeouts and error handling
- Ship the app to a public cloud or a private container, protected by a password
Prerequisites
- Python (course 02) and a trained model (course 04 or later)
- Basic familiarity with pandas DataFrames
Course Modules
- First steps and the Streamlit execution model
- Input and display components
- Layout: columns, tabs, sidebar
- Charts and tables
- Caching data and resources
- Session state and forms
- File upload and downloads
- Calling a model from the application
- Theme, appearance and usability
- Deployment and access control
The running example
Every module extends the same churn prediction dashboard used by a sales team: a form to score one customer on the spot, a searchable table of at-risk customers with sidebar filters, a few charts, a CSV upload for batch scoring, and a deployment that only the internal team can reach. The app grows in one file, then is split into pages once it justifies the move.
Assessment and certificate
The course ends with a 40-question exam covering every module. On success, a certificate of completion is issued; its number is verifiable on the platform.
Free courses, by contrast, end with a 5-question quiz and a preview of the certificate, without certification.