Intermediate · Systems

MLOps

Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.

TL;DR. A set of practices combining Machine Learning, DevOps, and Data Engineering to reliably and efficiently deploy and maintain ML systems.

Technical Definition

A set of practices combining Machine Learning, DevOps, and Data Engineering to reliably and efficiently deploy and maintain ML systems.

How it works

MLOps is a burgeoning discipline focused on industrializing machine learning workflows. It covers the entire lifecycle, from data collection and model development to deployment, monitoring, and governance. Effective MLOps ensures reproducibility, scalability, and seamless integration of AI models into business operations.

Related Concepts

• Data Pipeline — An automated workflow that ingests, transforms, and delivers data to models.
• Model Deployment — The process of making a trained machine learning model available for use in a production environment.