Introduction

MLOps Certified Professional (MLOCP) is a practical certification for engineers who want to build, deploy, monitor, and govern machine learning systems in production. It is offered by DevOpsSchool and focuses on real-world MLOps delivery rather than only theory.

What it is

MLOCP teaches the operational side of machine learning: how models move from notebooks into reliable production pipelines, how drift is detected, and how model services are monitored and rolled back safely. The program is delivered via the MLOps Certified Professional (MLOCP) curriculum and hosted on devopsschool.

Who should take it

This certification is best for DevOps engineers, SREs, platform engineers, cloud engineers, data engineers, and ML/AI practitioners who want production ML skills. It also fits technical managers who need to understand how ML systems are owned, governed, and operated.

Certification Overview

The MLOCP program is structured as a hands-on, demo-driven certification with practical assessments. In practical terms, it is designed to prove that you can own the full ML lifecycle, use the right tools for delivery and observability, and handle production issues like drift, rollback, governance, and scale. DevOpsSchool describes it as a 5-week, 100+ hour program with scenario-based evaluation and a real-world, open-book exam model.

Skills you'll gain

• ML pipeline design and automation.
• Model deployment and serving workflows.
• Containerization with Docker and orchestration with Kubernetes.
• CI/CD for machine learning systems.
• Model monitoring, drift detection, and rollback planning.
• Governance, reproducibility, and operational handoff.
• Infra automation with IaC and configuration management.
• Security practices for ML delivery.

Real-world projects you should be able to do

• Build an end-to-end training-to-serving pipeline.
• Deploy a model behind a reproducible CI/CD workflow.