用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/johnalbertini14-glitch/openclaw-skills --skill mlops-observability-cn命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
基于 SOC 职业分类
正在显示 SKILL.md
| name | mlops-observability-cn |
| version | 1.0.0 |
| description | Full stack observability - reproducibility, lineage, monitoring, alerting |
| license | MIT |
Glass box system - reproducible, traceable, monitored.
Complete tracking setup:
cp references/mlflow-tracking.py ../your-project/src/tracking.py
Tracks:
Using Evidently:
from evidently import Report
from evidently.metrics import DataDriftTable
report = Report(metrics=[DataDriftTable()])
report.run(reference_data=train, current_data=prod)
import shap
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X)
shap.summary_plot(shap_values, X)
# Copy tracking code
cp references/mlflow-tracking.py ./src/
# Add to training script:
# from tracking import setup_tracking, log_training_run
# 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)
plyer notificationsConverted from MLOps Coding Course