MLOps (Machine Learning Operations) is a systematic discipline that extends DevOps principles to machine learning systems, enabling teams to build, deploy, and maintain production-grade AI models at scale. It bridges the gap between experimental data science and reliable production systems through automation, continuous integration, and monitoring practices. In 2026, MLOps has evolved beyond basic model deployment to encompass sophisticated lifecycle management including drift detection, multi-cloud orchestration, governance frameworks, and emerging LLMOps practices for large language models—making it essential for organizations seeking to operationalize AI reliably and efficiently.