- How to utilize Streamlit to develop intuitive frontends for machine learning and data science applications, making your projects accessible to a wider audience
- The basics of Streamlit, including its installation and core features, tailored for beginners to quickly start building interactive web apps
- Integrating Large Language Models (LLMs) with Streamlit to create consumer-facing Q&A applications, leveraging the power of AI to answer user queries in real-time
- Transitioning from Jupyter Notebooks to a production-ready web app using Streamlit, enabling you to share your LLM-powered applications with the world beyond the developer community
Why Learn Streamlit?
Large Language Models (LLMs) are the latest technological revolution, and you’ve probably heard a lot about harnessing the power of LLMs to use them in AI application.
But in order to make your AI application easy to use for users, you’ll want a frontend that easily integrates with your LLM and provides a seamless experience for your users.
That’s where Streamlit comes in.
Streamlit is an amazing open-source Python library that provides a fast way to build and share machine learning and data science applications with the world.
This Project starts with a section that teaches you everything you need to know about Streamlit, specifically designed for beginners. Then in the second section we’ll jump into building the frontend for your LLM-powered Q&A App.
Wait… What’s a Project?
One of the most common things we hear from students is: “I want to build more projects!”.
We love hearing that, because building projects is really the best way to learn. And unique, challenging projects can really make your portfolio stand out for potential employers.
But also…it just feel so good when you actually build something real!
That’s why we’ve created ZTM Projects. A collection of comprehensive portfolio and practice projects that you can use to advance your knowledge, learn new skills, build your portfolio, and sometimes even just have fun!