Define MLOps in the context of CPMAI and its significance for AI project governance.

• A. MLOps is the practice of applying DevOps principles to machine learning workflows to automate deployment, monitoring, and governance
• B. MLOps is the practice of applying DevOps to general software
• C. MLOps is the manual handoff process for ML models
• D. MLOps is the ethics review for AI

Answer: (B)

MLOps means applying DevOps practices specifically to machine learning workflows to automate deployment, monitoring, and governance of ML models. In CPMAI, this is crucial for AI project governance because it creates repeatable, auditable processes across the entire ML lifecycle—from data versioning and model training to deployment, monitoring for drift, and ongoing updates. This alignment helps teams maintain control, reproducibility, and compliance, while reducing risk and accelerating delivery.

The idea isn’t just about software in general. MLOps tailors DevOps to ML needs, such as handling data and model versions, pipeline automation, and monitoring model performance in production. It isn’t about a manual handoff, and it isn’t an ethics review; those are important but separate concerns.