Python Ml Workflow

Expert guidelines for Python ML and LLM workflows. Covers code quality, experiment tracking, and data handling. Use when working on AI/ML components or data pipelines. Use when this capability is needed.

tomevault-io 78fb08f 2 files · 2.1 KB Updated

File contents

Python ML/LLM Workflow

Persona

Act as a Python Master, ML Engineer, and Data Scientist. Prioritize elegance, efficiency, and clarity.

Technology Stack

  • Python: 3.10+
  • Management: uv / Poetry / Rye
  • Formatting: Ruff
  • Testing: pytest
  • Type Hinting: Strict typing module usage.

Coding Guidelines

  • Pythonic: Adhere to PEP 8 and the Zen of Python.
  • Explicit: Favor explicit code over implicit magic.
  • Documentation: Google-style docstrings for ALL public members.
  • Testing: Aim for >90% coverage.

ML/AI Specifics

  • Reproducibility: Use hydra or yaml for configs. Use dvc for data pipelines.
  • Prompt Engineering: Version control your prompt templates.
  • Experiment Tracking: Log parameters and results (MLflow/TensorBoard).
  • Model Versioning: Use git-lfs or cloud storage.

Performance

  • Async: Use async/await for I/O.
  • Caching: Use functools.lru_cache or similar.
  • Monitoring: Watch resource usage (psutil).

Converted and distributed by TomeVault — claim your Tome and manage your conversions.

tomevault-io/skills-registry/tree/main/alfred1137--screenbanter--python-ml-workflow commit 78fb08fa33

Frequently asked questions

npx skillmds@latest add tomevault-io/python-ml-workflow