What role does Google Cloud Storage play in TensorFlow workflows on GCP?
- Google Cloud Storage serves as a scalable and durable repository for storing training data, model checkpoints, and other artifacts used in TensorFlow workflows on GCP.
- Google Cloud Storage provides built-in support for TensorFlow-specific data formats and APIs, optimizing data transfer and compatibility with TensorFlow frameworks.
- Google Cloud Storage enables real-time synchronization of training data between TensorFlow servers and client machines, ensuring consistency and availability of data for model training and evaluation.
- Google Cloud Storage provides built-in integration with TensorFlow Extended (TFX) components, streamlining the deployment and management of machine learning pipelines on Google Cloud.
Understanding the role of Google Cloud Storage in TensorFlow workflows on GCP is crucial for designing efficient and scalable machine learning pipelines that leverage cloud storage capabilities for data management and accessibility.
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