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.



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Ce que vous apprendrez
Plan, build, and iterate GenAI ideas quickly by turning a short Python script into an interactive Streamlit app that runs entirely inside Snowflake.
Strengthen your app with iterative development principles, practical prompt engineering methods, and RAG to ground responses in real data.
Publish your prototype to private Snowflake environments or Streamlit Community Cloud and refine confidently without extra infrastructure.
Compétences que vous acquerrez
- Catégorie : Interactive Design
- Catégorie : SQL
- Catégorie : Cloud Applications
- Catégorie : Application Development
- Catégorie : LLM Application
- Catégorie : Prompt Engineering
- Catégorie : Prototyping
- Catégorie : Agile Product Development
- Catégorie : User Interface (UI)
- Catégorie : Application Deployment
- Catégorie : Generative AI
Détails à connaître

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août 2025
3 devoirs
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Il y a 3 modules dans ce cours
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.
Inclus
18 vidéos8 lectures1 devoir
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.
Inclus
13 vidéos7 lectures1 devoir
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.
Inclus
14 vidéos7 lectures1 devoir
Instructeur

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Révisé le 23 août 2025
Very informative course where instructor not only teach interesting content, but also embed the product mindset.

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