Developing LLM App Frontends with Streamlit
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.
What you'll learn
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.
More Info
![Developing LLM App Frontends with Streamlit]()
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.
What you'll learn
- 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.
More Info
