OpenClaw

mlops-industrialization-cn

Transform prototypes into distributable Python packages

guohongbin-git0 来源安装数☆ 0 仓库 Starsv1.0.0

查看来源 ↗安装与使用 SKILL.md ↗

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求，再添加到工作流。

SkillHub CLI

skillhub install guohongbin-git/mlops-industrialization-cn

打开来源仓库，查看说明和兼容环境后，按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent，让它先读取说明、核对依赖，并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

MLOps Industrialization 🏭

Convert notebooks to production packages.

Features

1. Package Structure Generator 📦

Create src/ layout:

./scripts/create-package.sh my_package

Creates:

src/my_package/
├── __init__.py
├── io/          # I/O operations
├── domain/      # Pure business logic
└── application/ # Orchestration

2. Three-Layer Architecture 🏗️

Domain (Pure)

No I/O, no side effects
Feature transformations
Pure functions or immutable objects

I/O (Impure)

External interactions
Load data, save models
Classes for state management

Application

Wire Domain + I/O
Training loops, inference

Quick Start

# Create package structure
./scripts/create-package.sh my_ml_package

# Add CLI entrypoint to pyproject.toml:
# [project.scripts]
# train = "my_ml_package.application.train:main"

Key Files

Generated files:

src/my_package/domain/features.py - Feature engineering
src/my_package/io/data.py - Data loading/saving
src/my_package/application/train.py - Training pipeline

Author

Converted from MLOps Coding Course

Changelog

v1.0.0 (2026-02-18)

Initial OpenClaw conversion
Added package generator

来源信息

Source: github
Repository: openclaw/skills
Path: skills/guohongbin-git/mlops-industrialization-cn
Version: 1.0.0
Commit: ace2247024527cc2600616c46473847b693d8c30
分类来源: rules
状态: active
可见性: public

这些信息来自收录时的来源分析，可能滞后；使用前请核对最新来源文件。

来源内容

SKILL.md

# MLOps Industrialization 🏭

Convert notebooks to production packages.

## Features

### 1. Package Structure Generator 📦

Create `src/` layout:

```bash
./scripts/create-package.sh my_package
```

Creates:
```
src/my_package/
├── __init__.py
├── io/          # I/O operations
├── domain/      # Pure business logic
└── application/ # Orchestration
```

### 2. Three-Layer Architecture 🏗️

**Domain (Pure)**
- No I/O, no side effects
- Feature transformations
- Pure functions or immutable objects

**I/O (Impure)**
- External interactions
- Load data, save models
- Classes for state management

**Application**
- Wire Domain + I/O
- Training loops, inference

## Quick Start

```bash
# Create package structure
./scripts/create-package.sh my_ml_package

# Add CLI entrypoint to pyproject.toml:
# [project.scripts]
# train = "my_ml_package.application.train:main"
```

## Key Files

Generated files:
- `src/my_package/domain/features.py` - Feature engineering
- `src/my_package/io/data.py` - Data loading/saving
- `src/my_package/application/train.py` - Training pipeline

## Author

Converted from [MLOps Coding Course](https://github.com/MLOps-Courses/mlops-coding-skills)

## Changelog

### v1.0.0 (2026-02-18)
- Initial OpenClaw conversion
- Added package generator

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