MLOps

MLOps is DevOps for machine learning — the practices that take a model from a notebook to a reliable, monitored, continuously improving production service. A model that isn’t deployed, observed, and maintained delivers no value, however good its offline metrics.

Why this matters even for LLM work

You may never train a model, but you will absolutely operate one. Versioning, monitoring, evaluation pipelines, and safe rollout are exactly the practices that keep an LLM feature from silently degrading in production.

In this section

The ML Lifecycle The end-to-end lifecycle — data, experimentation, versioning, reproducibility, and CI/CD for ML.

Deployment & Monitoring Deployment patterns, the model registry, monitoring, drift detection, and retraining.

LLMOps How operating LLM systems differs — prompt versioning, eval-driven development, tracing, and cost control.

What you’ll be able to do

Describe the full lifecycle of a production model, deploy one safely, monitor it for degradation, and apply the same operational discipline to LLM-based systems.

Prerequisites

Machine Learning and AI System Design.