MLOps Observability

Full stack observability - reproducibility, lineage, monitoring, alerting

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

Changelog

v1.0.0 (2026-02-18)

• Initial OpenClaw conversion
• Added MLflow tracking code