Machine Learning Model Deployment with Streamlit

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 StreamlitAdd interactive elements, like buttons, forms, sliders, input elements, etc.Display chartsCustomize the layout of your application Capstone project: build an interactive dashboardCachingPerformance enhancement with cachingBasic and advanced usage of cachingCapstone project: deploy a classification modelSession state managementAdd more interactivity and boost performance with session state managementBasic and advanced usage of session stateCapstone project: deploy a regression modelMultipage applicationsBuild large apps with multiple pagesCapstone project: train and rank classification modelsAuthenticationAdd a security layer with authenticationAdd login/logout componentsAdvanced authentication with user management, reset password, etc.Capstone project: deploy a clustering model for marketingConnect to data sourcesConnect to databasesAccess data through APIsCapstone project: Deploy a sales demand modelDeploymentDeploy a Streamlit app for freeAdvanced deployment process with