MLOps

MLOps (Machine Learning Operations) is the discipline of applying DevOps principles — automation, continuous integration, and monitoring — to the full lifecycle of machine learning systems, from data ingestion and model training through deployment and ongoing production monitoring. It bridges the gap between data science experimentation and reliable, scalable software engineering in production. Practitioners use tools such as MLflow, Kubeflow, Docker, and cloud-native pipelines to version models, orchestrate workflows, and detect model drift.

AI companies increasingly recognize that building a model is only a small fraction of the work; the hard part is keeping models reliable, reproducible, and safe in production at scale, which is precisely what MLOps addresses. Demand for engineers who can own the full model lifecycle — not just training — has grown sharply as organizations move from proof-of-concept AI to production systems that directly affect business outcomes. Regulatory pressures such as the EU AI Act also require auditability and governance practices that MLOps workflows are designed to provide.