mlops-industrialization-cn

What This Skill Does

The mlops-industrialization-cn skill is a robust toolkit designed to bridge the gap between experimental data science notebooks and production-ready Python environments. It provides a standardized framework for code modularization by enforcing a clean, three-layer architectural pattern. By automating the creation of source layouts, it ensures that your machine learning projects are maintainable, testable, and distributable as proper Python packages. The skill leverages a Domain-IO-Application structure, which strictly separates core business logic (Domain), data-handling side effects (I/O), and high-level execution flows (Application). This decoupling is essential for CI/CD workflows, unit testing, and long-term project viability.

Installation

To integrate this skill into your environment, use the OpenClaw command line interface:

clawhub install openclaw/skills/skills/guohongbin-git/mlops-industrialization-cn

Once installed, ensure your terminal environment is configured to execute shell scripts, as the package generator relies on internal automation scripts to scaffold your directory structure.

Use Cases

This skill is ideal for:

1. Refactoring messy Jupyter Notebooks into production-grade pipelines.
2. Standardizing team-based ML projects to ensure consistent file organization.
3. Creating distributable Python wheels for model deployment.
4. Implementing rigorous architectural boundaries in complex deep learning pipelines to avoid 'spaghetti' code.

Example Prompts

1. "OpenClaw, initialize a new production package called 'customer_churn_predictor' using the mlops-industrialization-cn structure."
2. "I have my logic in a notebook; can you help me move the feature engineering functions into the domain layer defined by the MLOps skill?"
3. "Show me how to update my pyproject.toml to include a CLI entrypoint for my new training script created via the industrialization skill."

Tips & Limitations

To maximize effectiveness, always keep your 'Domain' layer pure; avoid importing heavy libraries like pandas or torch inside this layer if you can keep calculations to standard numerical logic, as this facilitates faster unit testing. Note that this skill primarily handles structural boilerplate and architecture patterns; it does not automatically refactor existing code, so you will need to manually move your legacy snippets into the generated files. Ensure your local environment is configured with pip and basic build tools for the best experience.