Tech Stack & Tooling

Detail the technologies, services, and infrastructure used to deliver this project. Keep it current as architecture evolves.

Languages & Frameworks

• Python 3.11+ – primary language for data processing and modelling.
• (Optional) R, SQL, Scala – document if applicable.
• Visualization – Matplotlib, Seaborn, Plotly (list whichever you adopt).

Core Libraries

• Data manipulation: pandas, NumPy.
• Machine learning: scikit-learn, XGBoost, LightGBM, TensorFlow/PyTorch (specify).
• Feature engineering: category encoders, pipeline utilities.
• Evaluation: scikit-learn metrics, statsmodels.

Infrastructure & Services

• Data storage: S3 buckets, Azure Blob, GCS, PostgreSQL, etc.
• Compute: local dev machines, cloud instances, Databricks, Spark clusters.
• Workflow orchestration: Airflow, Prefect, Dagster.
• Experiment tracking: MLflow, Weights & Biases.
• Monitoring: custom dashboards, logging sinks.

Dev Tooling

• Poetry for dependency management and packaging.
• Ruff + mypy for linting, formatting, typing.
• Tox for environment orchestration.
• Pytest + pytest-cov for testing/coverage.
• Loguru for logging.
• Pre-commit hooks for consistent code quality.

DevOps & CI/CD

• Continuous integration (e.g., GitHub Actions) – describe jobs/pipelines if configured.
• Containerisation (Docker) – note base images, build process.
• Deployment targets (API service, batch jobs, dashboards).

Access & Security

• Secrets management (environment variables, vaults, AWS Secrets Manager).
• IAM roles/permissions needed for cloud resources.
• Audit logging and compliance requirements.

Hardware Requirements

• Local development specs (RAM, CPU, GPU if required).
• Production environment specs.

References

• Links to architecture diagrams, ADRs (Architecture Decision Records), or runbooks.
• Vendor documentation or internal wiki pages.