You are creating a model training pipeline to predict sentiment scores from text-based product reviews. You want to have control over how the model parameters are tuned, and you will deploy the model to an endpoint after it has been trained. You will use Vertex AI Pipelines to run the pipeline. You need to decide which Google Cloud pipeline components to use. What components should you choose?
To create a model training pipeline to predict sentiment scores from text-based product reviews using Vertex AI Pipelines while maintaining control over the model parameters, the components to choose are TextDatasetCreateOp, CustomTrainingJobOp, and ModelDeployOp. TextDatasetCreateOp is suited for handling text-based data and preparing it for training. CustomTrainingJobOp allows for full control over the training process, including specifying the model architecture and hyperparameter tuning. ModelDeployOp facilitates the deployment of the trained model to a Vertex AI endpoint for serving predictions, ensuring the model is operational for real-time or batch inference.
Text dataset -> TextDatasetCreateOp Control over parameters -> CustomTrainingJobOp
TextDatasetCreateOp: This component is specifically designed to create datasets from text-based data, essential for handling product reviews. CustomTrainingJobOp: This component provides full control over the training process, allowing you to specify model architecture, hyperparameter tuning strategies, and other training parameters, aligning with the requirement for control over model tuning. ModelDeployOp: This component streamlines model deployment to a Vertex AI endpoint for real-time or batch inference, enabling the trained model to serve predictions.
D AutoML uses a predefined set of hyperparameter values for each algorithm used in model training. We can not have a control over hyperparameter
D fits perfect
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.
why not others? A. TabularDatasetCreateOp and EndpointCreateOp: TabularDatasetCreateOp is designed for tabular data, not raw text. EndpointCreateOp creates an endpoint, but you need a model upload step before deployment (handled by ModelDeployOp). B. AutoMLTextTrainingOp: While AutoML offers convenience, it removes control over hyperparameter tuning, which you require. C. TabularDatasetCreateOp and AutoMLTextTrainingOp: Similar to option A, TabularDatasetCreateOp is not ideal for text data, and AutoML removes hyperparameter control.
"Text dataset -> TextDatasetCreateOp Control over parameters -> CustomTrainingJobOp"