FastAPI & Streamlit - Deployment & Interaction This combination allows scientists to move from a Jupyter Notebook to a production-ready system. FastAPI handles the backend (model serving, data processing), while Streamlit provides the frontend (interactive widgets, real-time plotting). FIRST: Verify Prerequisites When to Use FastAPI: - Serving Machine Learning models as REST APIs. - Creating microservices for heavy scientific computations. - Building backends that require high concurrency (async/await). - Automatically generating API documentation (Swagger/Redoc). Streamlit: - Building intera…