MLOps in Production: A Complete Guide

Playbook Overview

This playbook provides a comprehensive, end-to-end framework for building, deploying, and maintaining production ML systems. It combines battle-tested architectural patterns, operational best practices, and real-world lessons learned from shipping ML systems at scale.

Who This Is For

• ML Engineers and Data Scientists transitioning from notebooks to production systems
• MLOps Engineers building and managing ML infrastructure and platforms
• Tech Leads and Engineering Managers architecting scalable ML systems
• Platform Engineers responsible for enabling ML teams across the organization
• DevOps Engineers working with ML workloads and pipelines

What You Will Learn

By the end of this playbook you will have:

1. Production-first ML mindset: Learn to frame ML problems with deployment constraints in mind from day one, avoiding the common trap of "great offline metrics, zero business impact."
2. End-to-end MLOps architecture: Master the complete ML lifecycle from data sourcing through deployment, monitoring, and continuous improvement—with practical patterns for each stage.
3. Platform thinking: Understand when and how to build ML platforms that scale across teams, including build vs. buy decisions, capability design, and operational models.
4. Production ML workflows: Implement robust CI/CD for ML, handle training-serving skew, manage feature pipelines, and orchestrate complex ML workflows reliably.
5. Operational excellence: Deploy monitoring, observability, testing, and governance frameworks that catch issues before they impact users and maintain trust in ML systems.

A Note on This Playbook

In my 5 years of experience as a Machine Learning Engineer, I've noticed a significant gap between academic tutorials and the realities of production MLOps. Many guides stop at deploying a model in a FastAPI container, leaving aspiring engineers without the strategic frameworks and practical insights needed for building robust, end-to-end systems.

This playbook is a sincere attempt to provide a practitioner's blueprint for production machine learning, moving beyond the code to explore the critical decision-making, trade-offs, and challenges involved. My goal is to eventually expand this work into a comprehensive, project-based MLOps course.

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.