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+--- Thread: Developing LLM App Frontends with Streamlit (/Thread-Developing-LLM-App-Frontends-with-Streamlit)
Developing LLM App Frontends with Streamlit - AD-TEAM - 10-10-2024
![[Image: 53afc0d2a04f9f477dd72e5a252e739e.jpg]](https://i124.fastpic.org/big/2024/1010/9e/53afc0d2a04f9f477dd72e5a252e739e.jpg)
Developing LLM App Frontends with Streamlit
.MP4, AVC, 1920x1080, 30 fps | English, AAC, 2 Ch | 1h 43m | 279 MB
Instructor: Andrei Dumitrescu
This byte-sized course will teach Streamlit fundamentals and how to use Streamlit to create a frontend for your LLM-powered applications.
In this project-based course you'll learn to use Streamlit to create a frontend for an LLM-powered Q&A application. Streamlit is an open-source Python library that simplifies the creation and sharing of custom frontends for machine learning and data science apps with the world.
[b]What you'll learn[/b]
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.
![[Image: 7RzQg5Bl_o.jpg]](https://images2.imgbox.com/57/4b/7RzQg5Bl_o.jpg)
In this project-based course you'll learn to use Streamlit to create a frontend for an LLM-powered Q&A application. Streamlit is an open-source Python library that simplifies the creation and sharing of custom frontends for machine learning and data science apps with the world.
[b]What you'll learn[/b]
- 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.
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