Structure.txt

ml-ids/
├── data/               <-- Raw and processed data (Tracked by DVC, NOT Git)
├── notebooks/          <-- Just for initial EDA (Exploratory Data Analysis)
├── src/                <-- The Core ML logic
│   ├── data/           <-- Ingestion and preprocessing pipelines
│   ├── models/         <-- Training loops and MLflow integration
│   └── monitoring/     <-- Evidently AI drift calculation logic
├── api/                <-- The Serving logic
│   ├── main.py         <-- FastAPI endpoints (/predict, /health)
│   └── schemas.py      <-- Pydantic models (Data validation)
├── dashboard/          <-- The UI
│   └── app.py          <-- Streamlit code
├── tests/              <-- Unit tests (Pytest)
├── .github/workflows/  <-- CI/CD pipelines (YAML files)
├── Dockerfile          <-- Blueprint for your API container
└── docker-compose.yml  <-- Blueprint to run API, MLflow, and Dashboard together