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Machine Learning Model Deployment with Streamlit

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

     
  • 4.7
  •  |
  • Reviews ( 262 )
₹799

This Course Includes

  • iconudemy
  • icon4.7 (262 reviews )
  • icon7 total hours
  • iconenglish
  • iconOnline - Self Paced
  • iconcourse
  • iconUdemy

About 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 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

What You Will Learn?

  • Understand the core concepts and features of Streamlit.
  • Build interactive data-driven web applications to deploy your model.
  • Master the advanced features and integrations in Streamlit.
  • Apply the best practices and optimization techniques for Streamlit.
  • Connect your Streamlit app to data sources.
  • Deploy your Streamlit app for free.