M-AI-SELF-PC-

ML-first scaffold for rapid experimentation, training, and deployment.

Goals

• Fast iteration on models with reproducible experiments.
• Clear separation of data, training, serving, and ops.
• Infrastructure-ready structure for productionization.

Structure

docs/           Architecture and planning docs
data/           Data contracts, schemas, samples
models/         Model artifacts and checkpoints (gitignored)
notebooks/      Exploratory notebooks
scripts/        Utility scripts for data and training
src/            Application and ML code
infra/          Deployment, CI/CD, and infra configs
tests/          Automated tests

Quickstart

1. Copy .env.example to .env.
2. Create and activate virtualenv.
3. Install package with dev dependencies:

   pip install -e .[dev,ml]

4. Train baseline model:

   python -m src.train

5. Run API:

   uvicorn src.serve.app:app --reload

6. Execute tests:

   pytest

Next step

See docs/next-steps.md for a practical roadmap (2 weeks, 6 weeks, 3 months).

Zarządzanie zadaniami

• Backlog wykonawczy: TASKS.md
• Roadmapa etapów: docs/next-steps.md

MLflow tracking

• Training automatycznie loguje parametry, metryki i artefakt modelu przez MLflow (jeśli extra ml jest zainstalowany).
• Gdy MLflow nie jest dostępny, trening działa dalej z fallbackiem (Noop tracker).

Agent bootstrap

• Szybki start agenta: ./scripts/setup_agent.sh
• Instrukcja auto-update klienta: docs/agent-setup.md

## Quickstart (suggested)
1. Copy `.env.example` to `.env`.
2. Create a virtualenv and install dependencies.
3. Run a baseline training job: `python -m src.train`.
4. Serve the model: `uvicorn src.serve.app:app --reload`.
5. Start with `docs/architecture.md` and `docs/plan.md`.