How can I serve multiple models using TensorFlow Serving?
You can serve multiple models by using the TensorFlow Serving's model configuration file to specify each model's path and version.
Can I update a model without taking down the server?
Yes, TensorFlow Serving allows you to update models dynamically without downtime by using the REST API to load new versions.
What formats do the models need to be in?
Models should be saved in the TensorFlow SavedModel format, which is the default format for TensorFlow Serving.
Is there a way to monitor the performance of the models?
Yes, you can use TensorFlow Serving's built-in metrics or integrate with monitoring tools like Prometheus to track performance.
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