MLOps Observability & SLA Playbook — Premium Bundle

Stop silent model failures. The exact validation rules, drift thresholds, and incident playbooks production ML teams use — with the working Python code to enforce them. Deploying a model is 20% of the work. Keeping it honest in production is the other 80% — and most teams find out something broke only after it cost them money. This bundle gives you the complete framework: The playbook (PDF): data validation SLAs (missingness, schema, latency, freshness, outliers), model benchmarking (precision, PSI drift bands, inference cost, fairness parity), and incident response rules — circuit breakers, shadow retraining, and the 60-second N-1 rollback mandate. The one-page SLA cheat sheet (PDF): every threshold on a single page for the on-call runbook. Working Python code: a Great Expectations validation suite that halts bad data before inference, and a drift monitor that scores PSI per feature and exposes Prometheus metrics for Grafana. Both run out of the box on demo data and adapt to your pipelines in minutes. Who it's for: Data scientists, MLOps engineers, and technical leads shipping ML to production — especially small teams that don't have a dedicated observability platform yet. What's included: MLOps Observability & SLA Playbook (PDF); SLA Cheat Sheet (PDF); data_validation.py — Great Expectations suite + Airflow notes; drift_monitor.py — PSI scoring + Prometheus endpoint; requirements.txt — tested dependency versions; Editable Markdown sources for both documents. License: One purchase covers one team. Adaptation for your own pipelines is encouraged; redistribution and resale are not permitted. Company-wide licensing available on request.