Building a machine learning model is only the first step. Deploying it into production, monitoring its performance, and updating it as data evolves are equally critical. This is where MLOps comes in. MLOps combines data engineering, development, and DevOps practices to manage the model lifecycle end‑to‑end.

Developers use tools such as MLflow, Kubeflow, Airflow, and cloud AI platforms to track experiments, deploy models as APIs, schedule retraining, and version control model artifacts. Continuous training pipelines ensure that models adapt to new data without manual intervention.

MLOps roles include ML Engineer, Data Engineer, and AI Operations Specialist. These professionals ensure that models perform reliably at scale. As AI adoption increases across industries, scalable and automated model deployment is becoming a core requirement in enterprise environments. MLOps careers are expanding rapidly with strong long‑term growth potential.