The correct answer is E. a, b and c only because MLOps includes workflow automation, continuous integration, and continuous deployment as important practices for managing the machine learning lifecycle. MLOps, or Machine Learning Operations, applies DevOps-style principles to machine learning systems so models can be developed, tested, deployed, monitored, and maintained in a reliable and repeatable way.

Workflow automation is part of MLOps because machine learning pipelines often include data ingestion, data validation, feature engineering, model training, model evaluation, deployment, and monitoring. Continuous integration is also included because ML code, data pipelines, configuration files, and model components need regular testing and validation when changes are made. Continuous deployment is another key part because approved models should be deployed efficiently into production environments with version control, rollback options, and monitoring.

Since all three options describe important MLOps capabilities, the best answer is E. a, b and c only.