Google Professional Machine Learning Engineer
Get started today
Ultimate access to all questions.
Your team is developing a machine learning (ML) solution on Google Cloud to classify customer support requests for a digital platform. The solution must leverage TensorFlow for model development to ensure full control over the model's code, serving, and deployment, with Kubeflow pipelines as the ML platform. Given the project's constraints, including the need for rapid development, cost efficiency, and the ability to customize the model for specific support request nuances, which approach should you take to build the classifier? Choose the best option.
Your team is developing a machine learning (ML) solution on Google Cloud to classify customer support requests for a digital platform. The solution must leverage TensorFlow for model development to ensure full control over the model's code, serving, and deployment, with Kubeflow pipelines as the ML platform. Given the project's constraints, including the need for rapid development, cost efficiency, and the ability to customize the model for specific support request nuances, which approach should you take to build the classifier? Choose the best option.
Real Exam