What is the architecture of TensorFlow Serving API?
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What is the architecture of TensorFlow Serving API?
TensorFlow Serving API is designed for serving machine learning models in production. It has a modular architecture that includes components like the model server, gRPC and REST APIs, and a model repository.
Can you explain the role of the model server in this architecture?
The model server is the core component that loads and manages the machine learning models. It handles requests, routes them to the appropriate model, and returns predictions.
What are the benefits of using TensorFlow Serving?
Some benefits include easy model deployment, support for multiple models, versioning, and efficient resource management.
How does TensorFlow Serving handle model versioning?
TensorFlow Serving allows you to manage multiple versions of a model simultaneously, enabling seamless updates and rollbacks without downtime.
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