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.


Empfohlene Erfahrung
Was Sie lernen werden
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.
Kompetenzen, die Sie erwerben
- Kategorie: Prompt Engineering
- Kategorie: Prototyping
- Kategorie: User Interface (UI)
- Kategorie: Data Integration
- Kategorie: User Acceptance Testing (UAT)
- Kategorie: Application Development
- Kategorie: Agile Product Development
- Kategorie: Natural Language Processing
- Kategorie: Unstructured Data
- Kategorie: Application Deployment
- Kategorie: Generative AI
Wichtige Details

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August 2025
3 Aufgaben
Erfahren Sie, wie Mitarbeiter führender Unternehmen gefragte Kompetenzen erwerben.

In diesem Kurs gibt es 3 Module
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.
Das ist alles enthalten
18 Videos7 Lektüren1 Aufgabe
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.
Das ist alles enthalten
13 Videos7 Lektüren1 Aufgabe
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.
Das ist alles enthalten
14 Videos7 Lektüren1 Aufgabe
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Dozent

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