Description
Machine Learning Model Deployment with Streamlit Course. Complete course on deploying machine learning models with Streamlit. Build web apps with ML and AI and share them with the world. This course will take you from the basics to deploying scalable applications powered by machine learning. To test your knowledge, I have designed more than six original projects with fully guided solutions. This course includes:
Streamlit Basics
- Add interactive elements such as buttons, forms, sliders, input elements, etc.
- Show diagrams
- Customize your app layout
- Capstone Project: Create an interactive dashboard
save
- Improved performance with cache
- Basic and advanced cache usage
- Capstone Project: Providing a classification model
Manage session status
- Add more interaction and increase performance by managing session state
- Basic and advanced use of session mode
- Capstone Project: Deploying a Regression Model
Multi-page applications
- Create 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, password reset, etc.
- Capstone Project: Providing a Clustering Model for Marketing
Connect to data sources
- Connect to databases
- Access data via APIs
- Capstone Project: Deploying a Sales Demand Model
extension
- Run Streamlit app for free
- Advanced deployment process with managed secrets and environment variables
What you will learn in the course “Machine Learning Model Deployment with Streamlit”
-
Understand the key concepts and features of Streamlit
-
Create interactive data-driven web applications to deploy your model
-
Master Streamlit’s advanced features and integrations
-
Apply best practices and optimization techniques to Streamlit
-
Connect your Streamlit app to data sources
-
Run your own Streamlit app for free
This course is suitable for people who
- Data scientists and machine learning engineers want to deploy ML models and dashboards.
Specifications of the Machine Learning Model Deployment with Streamlit course.
- Editor: Udemy
- Teacher: Marco Peixeiro
- Training level: beginner to advanced
- Training duration: 7 hours and 13 minutes
- Number of courses: 44
Headlines from the course “Machine Learning Model Deployment with Streamlit”.
Prerequisites for the course “Machine Learning Model Deployment with Streamlit”.
- Basic knowledge of Python and machine learning is required.
- This course focuses solely on deploying models using Streamlit. We will not take the time to explain how the models work or how to develop and train them.
- A computer with Anaconda installed.
- Your preferred text editor installed (I use VSCode)
Course pictures
Sample video of the course
installation Guide
After extracting, you can watch it with your favorite player.
Subtitles: None
Quality: 720p
Download link
free download software
Size
2.6GB