Description
Machine Learning Model Deployment with Streamlit course. Complete course on deploying machine learning models using Streamlit. Build web apps with ML and AI and use them to share them with the world. This course takes 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 charts
- Customize your app layout
- Capstone Project: Build an Interactive Dashboard
save
- Improved performance with cache
- Basic and advanced use of cache
- Capstone Project: Deploying 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
- Build large apps with multiple pages
- Capstone Project: Train and Rank Classification Models
Authentication
- Add a layer of security with authentication
- Add login/logout components
- Advanced authentication with user management, password reset, etc
- Capstone Project: Deploying a Clustering Model for Marketing
Connect to data sources
- Connect to databases
- Access data through APIs
- Capstone Project: Deploying a Sales Demand Model
expansion
- Run the Streamlit app for free
- Advanced deployment process with managed secrets and environment variables
What you will learn in the Machine Learning Model Deployment with Streamlit course
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Understand the main concepts and features of Streamlit
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Build interactive data-driven web applications to deploy your model
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Master Streamlit’s advanced features and integrations
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Apply best practices and optimization techniques to Streamlit
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Connect your Streamlit app to data sources
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Run your own Streamlit app for free
This course is suitable for people who
- Data scientists and machine learning engineers are looking to deploy ML models and dashboards.
Specifications of the Machine Learning Model Deployment with Streamlit course
- Publisher: Udemy
- teacher: Marco Peixeiro
- Training level: beginner to advanced
- Training duration: 7 hours and 13 minutes
- Number of courses: 44
Course headings Machine Learning Model Deployment with Streamlit
Prerequisites of the Machine Learning Model Deployment with Streamlit course
- A working knowledge of Python and machine learning is required.
- This course focuses only on deploying models using Streamlit. We will not spend time explaining how the models work or how they are developed and trained.
- A computer with Anaconda installed.
- Your favorite text editor installed (I use VSCode)
Course images
Sample video of the course
Installation guide
After Extract, view with your favorite Player.
Subtitle: None
Quality: 720p
download link
File(s) password: www.downloadly.ir
Size
2.6 GB
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