Essential Statistics for Production ML

Playbook Overview

This playbook distills essential statistical concepts into a practical framework for ML engineers and practitioners deploying production systems. It bridges the gap between theoretical statistics and real-world MLOps challenges, focusing on what you actually need to monitor models, run experiments, and make data-driven decisions.

Who This Is For

• ML Engineers building production systems who need statistical foundations
• Data Scientists transitioning from notebooks to production deployments
• MLOps practitioners responsible for monitoring, drift detection, and A/B testing
• Engineering teams that need a practical reference for statistical decision-making

What You Will Learn

By the end of this playbook you will have:

1. A solid grasp of the statistical foundations: random variables, distributions, moments, and quantiles that underpin all ML monitoring.
2. Practical knowledge of key distributions (Normal, Student-t, Binomial, Poisson, Exponential) and when they appear in production ML systems.
3. A framework for hypothesis testing, statistical distance measures, and how to apply them to detect model drift and data quality issues.
4. A complete playbook for running A/B tests on ML models, from experiment design to statistical analysis.
5. Operational patterns for monitoring production systems, detecting drift, and quantifying uncertainty in model predictions.
6. Decision-making frameworks that incorporate calibration, confidence intervals, and statistical rigor.

A Note on This Playbook

Important Disclaimers:

• On Authenticity: The methodologies and frameworks shared here are drawn directly from my professional experience.
• On Collaboration: These posts were created with the assistance of AI for diagram, code and prose generation. The strategic framing, project context, and real-world insights that guide the content are entirely my own.