Fast Prototyping of GenAI Apps with Streamlit tackles a costly problem: ideas lose momentum when they linger in discussions, drawn-out specifications, and intangibles that slow down the decision-making process. In a field where new GenAI capabilities surface every week, the teams that can show working demos first are the ones that influence roadmaps and win resources.
This course gives you that speed advantage.
You’ll explore how GenAI streamlines the prototyping workflow, facilitates rapid iteration and validation of product-market fit, and allows anyone, regardless of coding experience, to participate in the app creation process.
You’ll learn to turn a few lines of Python into a shareable Streamlit web app, cut down iteration time from weeks to hours using Snowflake’s secure data and coding copilot, and improve the performance of your application easily using Snowflake’s Cortex AI, a fully managed suite of LLMs, RAG, and text-to-SQL services (free 120-day trial included).
You’ll start with a basic chatbot, layer on prompt engineering and RAG, and publish the result to Snowflake, or Streamlit Community Cloud for real-time feedback.
By course end, you’ll leave with a working GenAI app, a repeatable MVP-first framework, and the skills to validate any new idea as soon as it strikes.
Learn how to rapidly transform ideas into working GenAI prototypes using Streamlit and Snowflake. You'll build your first Gen AI-powered app with real data, master the MVP mindset for fast development, and discover how generative AI accelerates coding, debugging, and prototyping workflows.
What's included
18 videos8 readings1 assignment
Show info about module content
18 videos•Total 85 minutes
Conversation between Chanin Nantasenamat and Andrew Ng •9 minutes
Introduction to Prototyping Generative AI Applications•2 minutes
The Benefits of Prototyping•6 minutes
How GenAI Revolutionized Prototyping•4 minutes
The Prototyping Development Cycle for GenAI•5 minutes
Avoiding Common Pitfalls•4 minutes
Introducing the Course Project and Dataset•4 minutes
Scoping an MVP•6 minutes
Overview of the Course Github Repo •4 minutes
Lab 1: Co-creating an MVP Plan with GenAI•6 minutes
Choosing the Right Tools•2 minutes
Setting up Your Environment •8 minutes
Getting Started with Streamlit•4 minutes
Making Your First Interactive Streamlit App•5 minutes
Integrating GenAI for Data Handling•5 minutes
Data Visualization•4 minutes
Publish Your App Online •5 minutes
Lab 2- Avalanche Sentiment Analysis Dashboard with GenAI•3 minutes
8 readings•Total 62 minutes
Setting Up Your Environment•5 minutes
Agile Prototyping•15 minutes
Lab 1 instructions: Co-creating an MVP Plan with GenAI•10 minutes
Join the DeepLearning.AI Forum to ask questions, get support, or share amazing ideas!•1 minute
Streamlit FAQ •10 minutes
Best Practices for Building GenAI Apps in Streamlit•10 minutes
Lab 2 instructions: Avalanche Sentiment Analysis Dashboard with GenAI•10 minutes
Module 1 lecture notes•1 minute
1 assignment•Total 30 minutes
Module 1 Quiz•30 minutes
Fast Prototyping with Streamlit in Snowflake
Module 2•4 hours to complete
Module details
Transform your basic prototype into a deployable, shareable MVP by integrating Snowflake Cortex for AI-powered insights, building interactive user interfaces, and mastering deployment to make your app accessible to real users. You'll parse unstructured data, create streamlined workflows, and learn multiple deployment options including Streamlit Community Cloud and Snowflake Native Apps.
What's included
13 videos7 readings1 assignment
Show info about module content
13 videos•Total 62 minutes
Building Prototypes in Snowflake•2 minutes
Introducing Snowflake•5 minutes
Snowsight Development Environment •7 minutes
From CSV to Cloud – Using Notebooks to Ingest Avalanche Data•13 minutes
Uploading a Batch of Files•4 minutes
From Stage to Table with Cortex•3 minutes
Extracting Information from the Content•3 minutes
Lab 1: Avalanche Shipping Analytics•5 minutes
One Table to Rule Them All•6 minutes
Sentiment Analysis with Cortex•3 minutes
Data Visualization in Snowflake•3 minutes
Building your AI-Powered Streamlit App Inside Snowflake•4 minutes
Lab 2 Overview: Using GenAI for Sentiment Analysis•3 minutes
7 readings•Total 141 minutes
Getting Started with Streamlit in Snowflake•10 minutes
Lab 2 instructions: Using GenAI for Sentiment Analysis•30 minutes
Module 2 lecture notes•1 minute
1 assignment•Total 30 minutes
Module 2 Quiz•30 minutes
Iterative Improvement
Module 3•3 hours to complete
Module details
Build intelligent data insights using Snowflake Cortex functions to automatically summarize, analyze sentiment, and extract patterns from customer reviews. You'll seamlessly combine structured and unstructured data, implement RAG (Retrieval-Augmented Generation) to enhance AI responses with real customer data, and use data augmentation techniques to improve answer quality—all without writing complex analytical logic, while preparing your app for real-world user testing.
What's included
14 videos7 readings1 assignment
Show info about module content
14 videos•Total 59 minutes
Choosing the Right Deployment Strategy•3 minutes
Deploying Your Prototype Internally in Streamlit•7 minutes
Deploying to Streamlit Community Cloud•3 minutes
Lab 1: Deploying Your Prototype•4 minutes
Iterate Quickly•3 minutes
Fast Feedback•3 minutes
Acting on Feedback•4 minutes
Iterate, Improve, Repeat – Fast Feedback for Your Avalanche App•4 minutes
Improving Prompts•11 minutes
Upgrading Your Prototype with Data Augmentation•4 minutes
Using RAG to Improve Model Performance•5 minutes
Setting up a RAG pipeline using Cortex Search•3 minutes
Lab 2: Integrating RAG into your chatbot•3 minutes
What comes next?•3 minutes
7 readings•Total 61 minutes
Connecting Streamlit Community Cloud to Snowflake•10 minutes
Lab 1 instructions: Deploying Your Prototype•5 minutes
Best Practices for Prompt Engineering with OpenAI•30 minutes
Lab 2 instructions: Integrating RAG into your chatbot•5 minutes
Module 3 lecture notes•1 minute
(Optional) Opportunity to mentor other learners•5 minutes
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DeepLearning.AI's expert-led educational experiences provide AI practitioners and non-technical professionals with the necessary tools to go all the way from foundational basics to advanced application, empowering them to build an AI-powered future.
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Is financial aid available?
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