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Answer: Create the pipeline using TensorFlow Extended (TFX) and standard TFX components. Orchestrate the pipeline using Vertex AI Pipelines.
The correct answer is B: Create the pipeline using TensorFlow Extended (TFX) and standard TFX components. Orchestrate the pipeline using Vertex AI Pipelines. TensorFlow Extended (TFX) is specifically designed for building end-to-end machine learning pipelines using TensorFlow, and it includes standard components like data validation and model analysis which are essential for ensuring data and model quality. Vertex AI Pipelines is a managed service in Google Cloud that integrates well with TFX, providing features such as monitoring, scheduling, and scaling, thereby minimizing development time and reducing infrastructure maintenance overhead.
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As a Machine Learning engineer, your responsibility is to design and implement training pipelines for ML models. Your current task involves creating an end-to-end training pipeline for a TensorFlow model. This model will be trained on several terabytes of structured data. The pipeline needs to include data quality checks before the training phase to ensure the integrity and accuracy of the data. Additionally, it must incorporate model quality checks after the training phase but before deployment to guarantee that the model meets performance standards. To meet business objectives, you need to minimize development time and reduce the necessity for infrastructure maintenance. Given these requirements, what is the best approach to build and orchestrate your training pipeline?
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A
Create the pipeline using Kubeflow Pipelines domain-specific language (DSL) and predefined Google Cloud components. Orchestrate the pipeline using Vertex AI Pipelines.
B
Create the pipeline using TensorFlow Extended (TFX) and standard TFX components. Orchestrate the pipeline using Vertex AI Pipelines.
C
Create the pipeline using Kubeflow Pipelines domain-specific language (DSL) and predefined Google Cloud components. Orchestrate the pipeline using Kubeflow Pipelines deployed on Google Kubernetes Engine.
D
Create the pipeline using TensorFlow Extended (TFX) and standard TFX components. Orchestrate the pipeline using Kubeflow Pipelines deployed on Google Kubernetes Engine.