What are some use cases for TensorFlow Serving API?

What are some use cases for TensorFlow Serving API?

TensorFlow Serving API is commonly used for deploying machine learning models in production. Some use cases include real-time predictions, batch predictions, and A/B testing of different models.

Can you explain how it handles model versioning?

Yes, TensorFlow Serving allows you to manage multiple versions of a model. You can specify which version to serve, enabling seamless updates and rollbacks.

What types of models can I deploy with it?

You can deploy various types of models, including TensorFlow models, Keras models, and even custom models built with other frameworks, as long as they are compatible.

Is it suitable for large-scale applications?

Absolutely! TensorFlow Serving is designed for high performance and can handle large-scale applications with ease, supporting multiple requests simultaneously.