Google Professional Machine Learning Engineer
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You are creating a machine learning model training pipeline to predict sentiment scores from text-based product reviews using Google's Vertex AI Pipelines. Your goal is to have control over the tuning of the model parameters and to deploy the model to an endpoint after training. Which set of Google Cloud pipeline components should you choose to achieve this?
Exam-Like
A
TabularDatasetCreateOp, CustomTrainingJobOp, and EndpointCreateOp
B
TextDatasetCreateOp, AutoMLTextTrainingOp, and EndpointCreateOp
C
TabularDatasetCreateOp. AutoMLTextTrainingOp, and ModelDeployOp
D
TextDatasetCreateOp, CustomTrainingJobOp, and ModelDeployOp
Explanation:
The correct components to choose are TextDatasetCreateOp, CustomTrainingJobOp, and ModelDeployOp. Here's why:
-
TextDatasetCreateOp: This component is specifically designed to handle text-based data like product reviews. It reads and prepares the text data for training the model.
-
CustomTrainingJobOp: Since you want control over hyperparameter tuning, a custom training job is the most suitable option. This component allows you to define your training script using a framework like TensorFlow and configure hyperparameters for optimization.
-
ModelDeployOp: After training, this component uploads the trained model to the Vertex AI Model Registry and deploys it to a Vertex AI Endpoint for serving predictions.
The other options are not ideal because:
- Option A includes TabularDatasetCreateOp, which is designed for tabular data, not raw text.
- Option B uses AutoMLTextTrainingOp, which removes control over hyperparameter tuning.
- Option C, like Option A, includes TabularDatasetCreateOp, which is not suited for text data, and AutoML removes hyperparameter control.
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