What is MLOps?

What is MLOps?

• A. A set of practices that combines ML, software engineering, and DevOps to streamline the lifecycle of ML models.
• B. A method for calibrating probabilities in ML models.
• C. A data preprocessing technique.
• D. A regulatory framework for AI.

Answer: (A)

MLOps is the set of practices that blends machine learning, software engineering, and DevOps to streamline the end-to-end lifecycle of ML models—from development and versioning through deployment, monitoring, and governance. It aims to make ML systems reliable, scalable, and repeatable by building automated pipelines, maintaining a model registry, enabling CI/CD for models, and continuously monitoring performance and data drift with mechanisms to retrain or rollback as needed. This focuses on the operational side of ML, not on probabilistic calibration, data preprocessing techniques, or regulatory frameworks. In short, MLOps is the engineered approach to deploying and maintaining ML models in production.

MLOps is the set of practices that blends machine learning, software engineering, and DevOps to streamline the end-to-end lifecycle of ML models—from development and versioning through deployment, monitoring, and governance. It aims to make ML systems reliable, scalable, and repeatable by building automated pipelines, maintaining a model registry, enabling CI/CD for models, and continuously monitoring performance and data drift with mechanisms to retrain or rollback as needed. This focuses on the operational side of ML, not on probabilistic calibration, data preprocessing techniques, or regulatory frameworks. In short, MLOps is the engineered approach to deploying and maintaining ML models in production.