This repository is dedicated to storing the code developed during the "Deploy ML models with Streamlit and share your data science work with the world" course on Udemy. The course covers basic to advanced techniques for deploying machine learning models using Streamlit, allowing you to share your data science work with the world.
For more details about the course, please refer to the link below:
Deploy ML models with Streamlit and share your data science work with the world
This course provides a comprehensive introduction to Streamlit and the process of deploying machine learning models, focusing on the following key areas:
- Streamlit Fundamentals: Understand the essential concepts and features of Streamlit, an open-source app framework for machine learning and data science teams.
- Building Interactive Web Applications: Learn how to develop interactive, data-driven web applications to deploy your models, allowing you to transform data scripts into shareable web apps in minutes.
- Advanced Features and Integrations: Master advanced Streamlit functionalities and integrations to enhance the interactivity and usability of your applications.
- Best Practices and Optimization Techniques: Apply best practices and optimization techniques to ensure your Streamlit applications are efficient and scalable.
- Connecting to Data Sources: Learn how to connect your Streamlit app to various data sources, including databases and APIs, to provide real-time and updated data.
- Deploying Streamlit Applications: Discover how to deploy your Streamlit applications for free, making it easy to share your work with others.
This course is ideal for data scientists and machine learning engineers who want to enhance their deployment skills, automate workflows, and efficiently manage machine learning applications in production environments.