01. MLOps Overview

MLOps—the practice of applying DevOps principles to machine learning systems—bridges the gap between data science experimentation and production-grade AI deployment. For local AI operators, MLOps isn't optional; it's the difference between one-off experiments and sustainable, auditable AI systems that survive beyond their creators.

Traditional DevOps handles code deployment. MLOps extends this to data, models, and the statistical processes that govern predictions. A model isn't static code—it's a living artifact whose behavior shifts with input distributions, feature engineering changes, and concept drift in your data. MLOps provides the infrastructure to track these changes, validate them systematically, and roll back when things go wrong.

The core MLOps cycle follows a familiar rhythm: develop, train, validate, deploy, monitor, and repeat. Each stage produces artifacts that feed the next. Experiment tracking captures the context of training runs. Model registries store versioned binaries with associated metadata. Pipeline orchestrators ensure reproducible execution. Validation gates enforce quality before deployment. Monitoring catches degradation in production.

Local verification checkpoint

Run the smallest example from this chapter in a local workspace and record the package version, runtime, data path, and observed output. If the result depends on model size, vector count, CPU/GPU backend, or available memory, note that constraint beside the exercise so the lesson remains reproducible.