MLOps moves machine learning projects from idea to production by applying practices such as versioning, CI/CD, testing, monitoring and reproducibility - all aimed at making ML models robust, reliable and maintainable.
By combining DevOps automation with ML-specific concerns like data management, hyperparameter tracking and model monitoring, MLOps teams build systems where models can be trained, deployed, updated and observed continuously. The philosophy is to treat ML models like software artifacts - with version control, tests and feedback loops.
In practice, MLOps may involve pipeline orchestration, data and model versioning, experiment tracking and deployment tools (e.g. Kubeflow, Seldon Core, DVC). It fosters collaboration between data scientists, engineers and operations teams to ensure ML solutions are scalable, reproducible and aligned with business goals.
