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As a junior Data Scientist at a rapidly growing startup, you are tasked with implementing MLOps practices to streamline the deployment of machine learning models developed using Python and TensorFlow on Vertex AI. Your team has compiled a comprehensive checklist of services and tools essential for establishing a robust MLOps pipeline. However, one item in the list does not align with the core principles of MLOps. Given the constraints of cost, compliance, and scalability, which service should be excluded from your MLOps checklist? Choose the one option that does not belong.
A
Continuous Integration and Continuous Delivery (CI/CD) pipelines for automating the testing and deployment of ML models
B
Source Control Tools like GitHub for version control and collaboration on ML model code
C
Data Pipelines for processing and transforming data at scale, ensuring data quality and consistency
D
Content Delivery Network (CDN) for caching and delivering static content to speed up web applications
E
Artifact Registry and Container Registry for managing Docker images and ML model artifacts