What is TensorFlow Serving API?
TensorFlow Serving API is a flexible, high-performance serving system for machine learning models, designed for production environments.
How do I deploy a model using TensorFlow Serving?
You can deploy a model by creating a Docker container with TensorFlow Serving and then using REST or gRPC to serve predictions.
What are the benefits of using TensorFlow Serving?
It provides easy model versioning, supports multiple models, and allows for efficient resource management.
Can I use TensorFlow Serving with other frameworks?
Yes, TensorFlow Serving can serve models from other frameworks as long as they are converted to TensorFlow format.
What are the common use cases for TensorFlow Serving?
Common use cases include real-time predictions, batch predictions, and serving models in microservices architectures.
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