Build AI-Powered Applications with Streamlit and RAG
Learn to build and deploy production-ready AI applications with Streamlit, integrate ML models, and monitor performance in real-world systems.
Skill level
IntermediateTime to complete
Average based on combined completion rates — individual pacing in lessons, projects, and quizzes may vary5 hoursProjects
2Prerequisites
2 coursesWe suggest you complete the following courses before you get started with Build AI-Powered Applications with Streamlit and RAG:- Intro to PyTorch and Neural Networks
- Intermediate Python 3
About this skill path
Building AI applications is one thing—deploying them for real users is another. In this course, you’ll learn how to create production-ready AI systems that are interactive, reliable, and trustworthy. You’ll master Streamlit to build user-friendly data applications, integrate models from HuggingFace and PyTorch, and develop RAG applications that let users chat with their own data. Beyond building, you’ll learn essential production skills: monitoring model performance, implementing data privacy protections, detecting model drift, and optimizing costs without sacrificing accuracy. By the end, you’ll be equipped to take AI projects from prototype to production with confidence.
Skills you'll gain
- Build and deploy interactive data applications using Streamlit’s core components
- Integrate AI/ML models from HuggingFace and PyTorch into Streamlit apps
- Build RAG (Retrieval Augmented Generation) applications that chat with your data
Syllabus
5 units • 5 lessons • 2 projects • 5 quizzes- 1
Welcome to Build AI-Powered Applications with Streamlit and RAG
Welcome to Build AI-Powered Applications with Streamlit and RAG!
- 2
Build AI Applications with Streamlit
Learn Streamlit to build and deploy interactive AI applications with Python in this hands-on course.
- 3
Creating AI Applications using Retrieval-Augmented Generation (RAG)
Learn how to give your large language model the powers of retrieval with RAG, and build a RAG app with Streamlit and ChromaDB.
- 4
Best Practices in AI Deployment
Learn machine learning operations best practices to deploy, monitor, and maintain production AI systems that are reliable, secure, and cost-effective.
- 5
Build AI-Powered Applications Next Steps
Build AI-Powered Applications with Streamlit and RAG Next Steps!
Certificate of completion available with Plus or Pro
Earn a certificate of completion and showcase your accomplishment on your resume or LinkedIn.
Projects in this skill path
- practice Project
Analyzing Recommendation System Performance Across Model Versions
Analyze the performance and cost-effectiveness of an e-commerce recommendation system across three model versions. You'll investigate whether newer models deliver better results, identify performance differences across customer segments, and determine which model version provides the best ROI. - practice Project
Build an AI Image Classification Dashboard with Streamlit
Build a professional web application for image classification using Streamlit and Hugging Face transformers, featuring real-time predictions, classification history, and analytics visualization.
Earn a certificate of completion
Show your network you've done the work by earning a certificate of completion for each course or path you finish.- Show proofReceive a certificate that demonstrates you've completed a course or path.
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Reviews from learners
- The progress I have made since starting to use codecademy is immense! I can study for short periods or long periods at my own convenience - mostly late in the evenings.ChrisCodecademy Learner @ USA
- I felt like I learned months in a week. I love how Codecademy uses learning by practice and gives great challenges to help the learner to understand a new concept and subject.RodrigoCodecademy Learner @ UK
- Brilliant learning experience. Very interactive. Literally a game changer if you're learning on your own.John-AndrewCodecademy Learner @ USA
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Related resources
- Article
How to Build RAG Pipelines in AI Applications?
Learn what RAG pipelines are, how they work, and build one using LangChain and ChromaDB. - Article
What is Retrieval-Augmented Generation (RAG) in AI?
Learn what RAG (retrieval-augmented generation) is, how it works, and build your own RAG application with LangChain and ChromaDB. - Article
What is Streamlit? A Complete Guide for Building Data Apps
Learn what Streamlit is, how to install it, and build your first interactive data app with Python, no web dev skills needed.
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What's included in skill paths
Practice Projects
Guided projects that help you solidify the skills and concepts you're learning.Assessments
Auto-graded quizzes and immediate feedback help you reinforce your skills as you learn.Certificate of Completion
Earn a document to prove you've completed a course or path that you can share with your network.