Machine Learning Operations (MLOps) is a set of practices that combines machine learning, DevOps, and data engineering to reliably deploy and maintain ML models in production environments. It addresses the unique challenges of ML systems, including data drift, model degradation, reproducibility, and the need for continuous retraining, while ensuring that models operate within ethical and regulatory boundaries.
From a compliance perspective, MLOps is increasingly important as regulations like the EU AI Act impose requirements for transparency, documentation, and human oversight of AI systems. A mature MLOps framework includes model versioning, automated testing pipelines, bias monitoring, and comprehensive audit trails that demonstrate how models were trained, validated, and deployed.