OpenClaw

mlops-observability-cn

Full stack observability - reproducibility, lineage, monitoring, alerting

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-observability-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 Observability 👁️

Glass box system - reproducible, traceable, monitored.

Features

1. MLflow Tracking 📊

Complete tracking setup:

cp references/mlflow-tracking.py ../your-project/src/tracking.py

Tracks:

Config (params)
Metrics (accuracy, loss)
Models (sklearn/pytorch)
Datasets (lineage)
Git commit (reproducibility)

2. Drift Detection 📉

Using Evidently:

from evidently import Report
from evidently.metrics import DataDriftTable

report = Report(metrics=[DataDriftTable()])
report.run(reference_data=train, current_data=prod)

3. Explainability (SHAP) 🔍

import shap

explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X)
shap.summary_plot(shap_values, X)

Quick Start

# Copy tracking code
cp references/mlflow-tracking.py ./src/

# Add to training script:
# from tracking import setup_tracking, log_training_run

Reproducibility

# Set all seeds
import random, numpy as np, torch
random.seed(42)
np.random.seed(42)
torch.manual_seed(42)

# Track git commit
import git
commit = git.Repo().head.commit.hexsha
mlflow.log_param("git_commit", commit)

Monitoring Checklist

Random seeds fixed
MLflow tracking enabled
System metrics logged
Drift detection setup
SHAP explanations saved
Alerts configured

Alerting

Local: plyer notifications
Production: PagerDuty (critical) / Slack (warnings)

Author

Converted from MLOps Coding Course

Changelog

v1.0.0 (2026-02-18)

Initial OpenClaw conversion
Added MLflow tracking code

来源信息

Source: github
Repository: openclaw/skills
Path: skills/guohongbin-git/mlops-observability-cn
Version: 1.0.0
Commit: f45c54896ff3d92bc553f9eed17e10524648cf09
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 Observability 👁️

Glass box system - reproducible, traceable, monitored.

## Features

### 1. MLflow Tracking 📊

Complete tracking setup:

```bash
cp references/mlflow-tracking.py ../your-project/src/tracking.py
```

Tracks:
- Config (params)
- Metrics (accuracy, loss)
- Models (sklearn/pytorch)
- Datasets (lineage)
- Git commit (reproducibility)

### 2. Drift Detection 📉

Using Evidently:

```python
from evidently import Report
from evidently.metrics import DataDriftTable

report = Report(metrics=[DataDriftTable()])
report.run(reference_data=train, current_data=prod)
```

### 3. Explainability (SHAP) 🔍

```python
import shap

explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X)
shap.summary_plot(shap_values, X)
```

## Quick Start

```bash
# Copy tracking code
cp references/mlflow-tracking.py ./src/

# Add to training script:
# from tracking import setup_tracking, log_training_run
```

## Reproducibility

```python
# Set all seeds
import random, numpy as np, torch
random.seed(42)
np.random.seed(42)
torch.manual_seed(42)

# Track git commit
import git
commit = git.Repo().head.commit.hexsha
mlflow.log_param("git_commit", commit)
```

## Monitoring Checklist

- [ ] Random seeds fixed
- [ ] MLflow tracking enabled
- [ ] System metrics logged
- [ ] Drift detection setup
- [ ] SHAP explanations saved
- [ ] Alerts configured

## Alerting

- **Local**: `plyer` notifications
- **Production**: PagerDuty (critical) / Slack (warnings)

## 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 MLflow tracking code

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