18. Capstone Project Framework

📋 Capstone Framework

1. Problem Selection: Identify a real problem with a measurable success metric. Define: who has this problem, how big is it, why hasn't it been solved, and what does success look like. A compelling problem statement is half the battle.
2. Data Strategy: Where does your training data come from? How much do you have? What is your annotation strategy? What are the data quality risks? Plan your data pipeline before writing model code.
3. Baseline First: Always implement the simplest possible approach first (a rule-based system, logistic regression, an LLM with zero-shot prompting). Measure its performance. Your sophisticated model must beat this baseline to justify its complexity.
4. Iterative Development: Build the pipeline end-to-end early (even with a toy model) to expose integration issues. Then improve each component systematically: better data → better features → better model → better serving.
5. Deployment: Deploy to a publicly accessible endpoint or web app. The project must be demonstrable—not just a notebook. Hugging Face Spaces, Streamlit Cloud, or AWS/GCP free tiers.
6. Documentation and Presentation: Write a thorough README. Create a 5-minute demo video. Write a technical blog post explaining your approach, what you tried that didn't work, and what you learned. This content is as important as the code.