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Answer: Construct the pipeline using TensorFlow Extended (TFX) with its standard components for data validation, transformation, and model evaluation, and orchestrate it using Vertex AI Pipelines to leverage Google Cloud's managed services and scalability., Combine the use of TensorFlow Extended (TFX) for pipeline construction with Vertex AI Pipelines for orchestration, and additionally implement custom monitoring hooks in Vertex AI for real-time compliance tracking.
The correct approach is to construct the pipeline using TensorFlow Extended (TFX) and standard TFX components, and orchestrate the pipeline using Vertex AI Pipelines. This combination offers several advantages: - **TensorFlow Extended (TFX)**: Provides a comprehensive framework for building and managing ML pipelines, including data ingestion, validation, transformation, training, model evaluation, and deployment. It includes built-in components for data validation and quality checks, ensuring data integrity, and offers metrics and visualizations for model evaluation. - **Vertex AI Pipelines**: Offers a fully managed platform for building, managing, and monitoring ML pipelines. It leverages Google Cloud’s infrastructure for scalability and reliability and integrates seamlessly with other GCP services, simplifying the pipeline setup. By using TFX and Vertex AI Pipelines, you can accelerate development by leveraging pre-built components and best practices, ensure quality through robust data quality checks and model evaluation, optimize performance by fine-tuning hyperparameters, and scale efficiently with Google Cloud’s infrastructure. Additionally, when E is available, combining TFX with Vertex AI Pipelines and implementing custom monitoring hooks in Vertex AI for real-time compliance tracking provides an enhanced solution that meets the financial institution's strict compliance requirements.
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As a Machine Learning Engineer at a large financial institution, you are tasked with developing a scalable and efficient training pipeline for a TensorFlow model designed to predict credit risk. The pipeline must process several terabytes of structured financial data, ensuring rigorous data quality checks before training and comprehensive model quality checks after training but before deployment. Given the institution's strict compliance requirements and the need to minimize both development time and infrastructure maintenance, which of the following approaches best meets these criteria? (Choose two options if E is available, otherwise choose one.)
A
Develop the pipeline using Kubeflow Pipelines DSL with custom components for data validation and model evaluation, and orchestrate it on a self-managed Kubernetes cluster to ensure compliance with internal data governance policies.
B
Construct the pipeline using TensorFlow Extended (TFX) with its standard components for data validation, transformation, and model evaluation, and orchestrate it using Vertex AI Pipelines to leverage Google Cloud's managed services and scalability.
C
Build the pipeline using Apache Beam with custom data processing and validation logic, and orchestrate it using Cloud Composer (managed Apache Airflow) for flexibility in scheduling and monitoring.
D
Implement the pipeline using TensorFlow Extended (TFX) for its built-in data and model validation capabilities, and orchestrate it using Kubeflow Pipelines on Google Kubernetes Engine (GKE) to maintain control over the infrastructure.
E
Combine the use of TensorFlow Extended (TFX) for pipeline construction with Vertex AI Pipelines for orchestration, and additionally implement custom monitoring hooks in Vertex AI for real-time compliance tracking.