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In the context of developing a scalable and efficient machine learning system, your team is tasked with automating and orchestrating ML pipelines to support continuous integration and deployment (CI/CD) practices. The system must adhere to strict compliance standards, minimize operational costs, and ensure high model accuracy. Considering these constraints, what is the main objective behind automating and orchestrating machine learning pipelines in this scenario? (Choose one correct option)
A
To significantly increase the workload and need for manual oversight by data scientists
B
To create data sets with high variability and unpredictability for model training
C
To enhance and automate the complete ML workflow from data preprocessing to model deployment, ensuring efficiency, consistency, and scalability
D
To reduce the precision of machine learning models by introducing automated errors