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Your business has been effectively utilizing machine learning models, with a significant portion developed using TensorFlow. Recently, to enhance efficiency and speed in the design process, AutoML has been adopted. You are now in search of a comprehensive environment that not only organizes and manages model training, validation, and tuning but also handles updating models with new data, distribution, and monitoring in production. Considering the need for scalability, cost-effectiveness, and integration with existing TensorFlow and AutoML workflows, which solution best meets these needs? (Choose two correct options if option E is available.)
A
Migrate all models to BigQueryML with AutoML, leveraging its SQL-like interface for model management.
B
Leverage Kubeflow Pipelines on Google Kubernetes Engine for orchestrating workflows, offering flexibility and scalability.
C
Adopt Vertex AI, which provides custom tooling and pipelines, integrating AutoML and custom models with comprehensive ML pipeline management.
D
Deploy TensorFlow on Kubernetes manually, ensuring full control over the environment but requiring significant operational overhead.
E
Migrate all models to AutoML Tables, focusing on tabular data and automated model selection and training.