MLOps

MLOps combines machine learning, DevOps, and data engineering practices to deploy and maintain ML models in production reliably and efficiently.

Common tools include MLflow, Kubeflow, Docker, Kubernetes, Airflow, DVC, and cloud-native services from AWS, GCP, and Azure.

DevOps experience helps but isn't strictly required. Understanding CI/CD concepts, containerization, and cloud infrastructure will accelerate your learning.

Most ML models fail in production due to data drift, scalability issues, or lack of monitoring. MLOps practices ensure models remain accurate, reliable, and maintainable over time.