Machine Learning Model Deployment with Streamlit

Deploy ML models with Streamlit and share your data science work with the world

The complete course to deploy machine learning models using Streamlit. Build web applications powered by ML and AI and deploy them to share them with the world.

This course will take you from the basics to deploying scalable applications powered by machine learning. To put your knowledge to the test, I have designed more than six capstone projects with full guided solutions.

This course covers:

Basics of Streamlit

• Add interactive elements, like buttons, forms, sliders, input elements, etc.

• Display charts

• Customize the layout of your application

• Capstone project: build an interactive dashboard

Caching

• Performance enhancement with caching

• Basic and advanced usage of caching

• Capstone project: deploy a classification model

Session state management

• Add more interactivity and boost performance with session state management

• Basic and advanced usage of session state

• Capstone project: deploy a regression model

Multipage applications

• Build large apps with multiple pages

• Capstone project: train and rank classification models

Authentication

• Add a security layer with authentication

• Add login/logout components

• Advanced authentication with user management, reset password, etc.

• Capstone project: deploy a clustering model for marketing

Connect to data sources

• Connect to databases

• Access data through APIs

• Capstone project: Deploy a sales demand model

Deployment

• Deploy a Streamlit app for free

• Advanced deployment process with secrets management and environment variables