Oreilly – Build Python Web Apps with Streamlit, Video Edition 2026-5

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Description

Build Python Web Apps with Streamlit, Video Edition. This course is a comprehensive, hands-on learning program for building interactive web applications using Python and the Streamlit framework, enabling developers to quickly publish their data tools and AI tools to the web without requiring front-end knowledge. This course is designed based on a proven, hands-on project-based approach to help Python programmers turn their ideas for data tools and AI workflows into web applications without requiring complex knowledge of HTML, CSS, and JavaScript. Using ready-made UI components and the simple configuration of the Streamlit framework, learners learn to understand implementation models and experience software engineering in action through real-world programming and debugging challenges. During this training program, participants will learn about RAG and Agent patterns by building a variety of data dashboards, interactive checklists, and AI-powered chatbots. Important topics such as database integration and persistent data storage, user authentication and access management, implementing production-grade architectures, and documenting software infrastructure are also covered. Designed by a senior software engineer, this course will help enthusiasts take their web development and AI skills to a professional level.

What you will learn

Build interactive web applications without having to learn HTML, CSS, and JavaScript.
A complete understanding of the Streamlit framework’s execution model and how it works behind the scenes
Working with databases and managing persistent data in web applications
Creating and implementing software architectures at the production standard level
Implementing authentication and user access level management
Building powerful applications powered by large LLM language models
Developing advanced data dashboards and functional information visualizations
Adhering to standard security principles and best software development practices

This course is suitable for people who:

Python programmers who want to build web applications without prior knowledge of the web or artificial intelligence.
Data developers who want to share their analysis results in the form of interactive dashboards
Those interested in learning modern artificial intelligence tools and language models in the Python framework.
People who want to learn software engineering and web deployment skills by building real projects

Build Python Web Apps with Streamlit, Video Edition course details

Publisher: Oreilly
Instructor: Aneev Kochakadan
Training level: Beginner to advanced
Training duration: 12 hours and 59 minutes

Course headings

Foreword
Part 1. Hitting the ground running
Chapter 1. Introduction to Streamlit
Chapter 1. What is Streamlit?
Chapter 1. Ten reasons Streamlit is so popular
Chapter 1. What can you build with Streamlit?
Chapter 1. When not to use Streamlit
Chapter 1. How is Streamlit different from other technologies?
Chapter 1. Summary
Chapter 2. Getting started with Streamlit
Chapter 2. Setting up your development environment
Chapter 2. Running Streamlit for the first time
Chapter 2. The Streamlit development workflow
Chapter 2. Building your first app
Chapter 2. Summary
Chapter 3. Taking an app from concept to code
Chapter 3. Stating our concept: A unit converter
Chapter 3. Defining the requirements
Chapter 3. Visualizing the user experience
Chapter 3. Brainstorming the implementation
Chapter 3. Writing code
Chapter 3. Iterating on the app
Chapter 3. Summary
Chapter 4. Streamlit’s execution model
Chapter 4. Implementing and troubleshooting the app
Chapter 4. How Streamlit executes an app
Chapter 4. Persisting variables across reruns
Chapter 4. Completing the app
Chapter 4. Summary
Chapter 5. Sharing your apps with the world
Chapter 5. Deploying the to-do list app to Streamlit Community Cloud
Chapter 5. Deploying an app that uses an external service
Chapter 5. Deploying changes to Streamlit Community Cloud
Chapter 5. Summary
Part 2. Diving into more complex apps
Chapter 6. A dashboard fit for a CEO
Chapter 6. Loading the data
Chapter 6. Prepping and filtering the data
Chapter 6. Calculating and displaying metrics
Chapter 6. Constructing visualizations
Chapter 6. Summary
Chapter 7. The CEO strikes back: Supercharging the dashboard
Chapter 7. Granularity in the time series chart
Chapter 7. Interdependent filters
Chapter 7. Date range comparisons
Chapter 7. A drilldown view
Chapter 7. Enabling deeplinks
Chapter 7. Sourcing data from a data warehouse
Chapter 7. Summary
Chapter 8. Building a CRUD app with Streamlit
Chapter 8. Setting up persistent storage
Chapter 8. Creating user accounts
Chapter 8. Setting up a multi-page login flow
Chapter 8. Creating, reading, updating, and deleting haikus
Chapter 8. Deploying Haiku Haven
Chapter 8. Summary
Part 3. Streamlit and LLMs
Chapter 9. Creating an AI-powered application
Chapter 9. Using AI to generate trivia questions
Chapter 9. Using AI to evaluate answers
Chapter 9. Moving through game states
Chapter 9. Game mechanics: Keeping score, a New Game button, and Game Over
Chapter 9. Including multiple questions
Chapter 9. Adding AI personalities
Chapter 9. Summary
Chapter 10. RAG and agentic apps with LangGraph and Streamlit
Chapter 10. Creating a basic chatbot
Chapter 10. Multi-turn conversations
Chapter 10. Restricting the bot to customer support
Chapter 10. Retrieval Augmented Generation
Chapter 10. Turning the bot into an agent
Chapter 10. Summary
Part 4. Testing and deploying apps
Chapter 11. Testing Streamlit apps
Chapter 11. Testing the application
Chapter 11. Unit testing the backend
Chapter 11. Unit testing the Streamlit frontend
Chapter 11. Testing the integration between components
Chapter 11. Testing the end-to-end flow
Chapter 11. Evolving code and tests
Chapter 11. Summary
Chapter 12. Packaging and deploying Streamlit apps
Chapter 12. Packaging the app using Docker
Chapter 12. Deploying to AWS Elastic Beanstalk
Chapter 12. Adding a custom domain and HTTPS
Chapter 12. Go forth and develop!
Chapter 12. Summary
Appendix A. Installing Python and Streamlit
Appendix A. Installing the right Python version
Appendix A. Installing Streamlit
Appendix A. Getting the streamlit command to work