Faysal AzizRapid Prototyping with Streamlit & Gradio
Turn Python code into interactive AI apps in hours, not weeks
Why this course
The gap between building a model and delivering something people can actually use is where many AI and data projects stall. Streamlit and Gradio solve this problem by allowing developers to create interactive, web-based applications directly from Python code, without traditional front-end complexity. These tools have become essential in modern AI teams for rapid prototyping, internal demos, proof-of-concepts, and even production-ready tools.
This two-day course focuses on practical, industry-driven workflows that show how professionals rapidly move from notebooks and scripts to usable applications. The instructor brings over 30 years of industry experience and teaches proven patterns used in real AI teams, avoiding academic abstractions in favor of what actually works under real constraints.
Learning outcomes
By the end of this course, learners will be able to:
- Understand the role of rapid prototyping in AI and data product development.
- Build interactive data and AI applications using Streamlit.
- Create lightweight ML and AI demos using Gradio.
- Design user inputs, outputs, and layouts for non-technical users.
- Integrate Python models, scripts, and pipelines into interactive apps.
- Handle basic state, performance, and user interaction challenges.
- Decide when to use Streamlit vs. Gradio based on use case.
- Deploy and share prototypes efficiently with stakeholders.
Prerequisites
Participants should have:
- Solid fundamentals in Python (functions, data structures, basic modules).
- Basic familiarity with data manipulation or machine learning workflows.
- Experience running Python scripts and installing packages.
10 modules
011. Why Rapid Prototyping Matters in AI Projects4 topics
- The gap between models and usable tools
- Prototyping vs. production systems
- Common AI project failure points
- Where Streamlit and Gradio fit in modern workflows
022. Foundations of Interactive Python Apps4 topics
- Event-driven thinking for Python developers
- Inputs, outputs, and reactive execution models
- Stateless vs. stateful application behavior
- Common UX mistakes in technical prototypes
033. Getting Started with Streamlit4 topics
- Streamlit application architecture
- Running and structuring a Streamlit app
- Core UI components
- Text, numbers, sliders, buttons
- File uploaders and selectors
- Layout and organization
- Columns, containers, and sidebars
044. Building Data & AI Prototypes with Streamlit4 topics
- Displaying data
- Tables, dataframes, and summaries
- Integrating Python logic
- Data processing pipelines
- Model inference workflows
- Visualization basics
- Charts and plots
- Handling user-driven recomputation
055. State, Performance & Usability in Streamlit5 topics
- Understanding reruns and execution flow
- Managing state across interactions
- Caching strategies
- Performance considerations for larger datasets and models
- Structuring code for maintainability
066. Introduction to Gradio5 topics
- Gradio’s design philosophy
- Component-based interface building
- Inputs and outputs
- Text, images, audio, files
- Function-first development model
- Quick demos vs. structured applications
077. Building AI Demos with Gradio5 topics
- Wrapping ML and AI functions
- Creating interactive model demos
- Handling preprocessing and postprocessing
- Managing multiple inputs and outputs
- Customizing layouts and behavior
088. Streamlit vs. Gradio: Choosing the Right Tool4 topics
- Strengths and limitations of each framework
- Typical industry use cases
- Decision criteria
- Audience
- Complexity
- Deployment needs
- Combining both tools in a workflow
099. Deployment & Sharing Prototypes4 topics
- Local vs. cloud-based deployment concepts
- Sharing apps with teams and stakeholders
- Security and access considerations
- Common deployment pitfalls
1010. Industry Patterns & Best Practices4 topics
- Designing prototypes for feedback, not perfection
- Making demos understandable to non-technical users
- Avoiding overengineering
- Transitioning from prototype to production systems
A programme built around your team.
Share your training goals and requirements.