Build a Private Smart Text Analyzer in 5 Days
Overview
This workshop bridges the gap between AI theory and real-world application. You'll get practical, hands-on experience building an AI-powered web app using open-source models in a controlled, private cloud setup. By the end, you'll have a fully functional smart text analyzer that performs sentiment analysis and named entity recognition.
Learning Goals
- Understand the modern AI ecosystem (APIs vs. Local Models).
- Set up a professional-grade, reproducible cloud development environment.
- Work hands-on with open-source AI models from Hugging Face.
- Build a reusable AI Analyzer module in Python.
- Create a full-stack web application integrating AI models.
- Apply UI/UX improvements to display AI results meaningfully.
Day-Wise Structure
Theme: Demystifying AI and setting up a professional, bulletproof development environment.
Key Outcome: Every attendee will successfully load an AI model and run their first inference.
- Introduction to the AI Landscape: APIs vs. Local Models.
- The Unbreakable Development Environment: Cloud IDEs & Dev Containers.
- Lab: Launching Your Environment & First Inference with the `transformers` library.
Theme: Moving beyond the magic and understanding the components.
Key Outcome: Attendees will be able to customize the model's input and process its output programmatically.
- Anatomy of a Transformer Pipeline (Tokenizer, Model, Post-processing).
- Building a Reusable AI Service in a Python class.
- Lab: Refactoring the Day 1 script into a reusable Analyzer module.
Theme: Bringing your AI model to life with a web interface.
Key Outcome: Attendees will have a working web app to type text and see the AI's analysis.
- Introduction to Web Frameworks (Flask/FastAPI).
- Creating an API Endpoint (`/analyze`).
- Lab: Building the Full-Stack App with a simple HTML/JS frontend.
Theme: Making the application smarter by adding a new skill.
Key Outcome: Attendees will add a second, different AI capability (NER) to their application.
- Beyond Sentiment: Introduction to Named Entity Recognition (NER).
- Integrating a Second Model into the application.
- Lab: Upgrading the Smart Analyzer with a new `/entities` endpoint and UI feature.
Theme: Polishing the project and revealing the professional journey ahead.
Key Outcome: A completed, polished project and a clear path forward.
- Capstone Challenge Lab (2 Hours): Implement UI/UX improvements (highlighting entities) and add new functionality.
- The Professional Horizon: Discussing fine-tuning, deployment, and advanced LLMs.
- The Pitch: Introduction to our 3-Month Intensive Program.
- Open Q&A session.
Requirements
Participants need only a laptop and a stable internet connection. All development tools and libraries will be provided via pre-configured cloud environments. Basic Python familiarity is helpful but not mandatory.
