Description
What is this course about?
In this hands-on course, instructor Megan Silvey shows you how to utilize Streamlit to build web applications. This open-source Python framework has become a key tool for data scientists and AI/ML engineers. In particular, learn how to design and deploy a retrieval‑augmented generation (RAG) document Q&A chatbot in Streamlit using Python and OpenAI’s API. Get started with an overview of Streamlit and large language models (LLMs), along with best practices for working with AI and APIs. Next, find out how to prepare text data to create a RAG pipeline that integrates into a Streamlit chat interface. By the end of this course, you’ll be prepared to test, maintain, and deploy a fully functional chatbot on Streamlit Community Cloud.
This course is integrated with GitHub Codespaces, an instant cloud developer environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time, all while using a tool that you’ll likely encounter in the workplace. Check out the “Using GitHub Codespaces with this course” video to learn how to get started.
Instructor
Who teaches this course?
Megan Silvey is a data science consultant and the founder of Silvey Solutions, which provides actionable, data-driven insights and has worked with the United Nations, Emeritus, phData, and BankOnIP.
Objectives
What will I be able to do by the end of this course?
- Properly prepare data and build a local vector store for a Streamlit chatbot.
- Implement a retrieval‑augmented generation (RAG) pipeline that retrieves context and queries an LLM for accurate answers.
- Create an interactive chat UI in Streamlit, manage chat history, and handle errors.
- Deploy and maintain a RAG‑powered chatbot on Streamlit Community Cloud.
Audience
Who is this course for?
- Python programmers in data science and machine learning looking for practical AI development skills
- Software developers interested in web application development with AI integration
- Professionals aiming to enhance their portfolio with AI-powered applications for potential employment opportunities
Prerequisites
What do I need to know before taking this course?
- Basic understanding of Python programming
- Familiarity with data analysis techniques
- Fundamental knowledge of machine learning principles