TensorFlow Serving provides a built-in mechanism for handling model versioning. Model versioning is important because it allows you to update and deploy new versions of your machine learning models without affecting the availability and performance of existing models.

  When using TensorFlow Serving, each model is assigned a unique version number. The version number can be any arbitrary string, such as an integer or a timestamp. This version number is used to distinguish between different versions of the same model.

  TensorFlow Serving supports serving multiple versions of a model simultaneously. This enables easy A/B testing or gradual rollout of new models. The serving system can route incoming requests to the desired model versions based on the version number specified.

  To handle model versioning in TensorFlow Serving, you need to specify the model version when making a prediction request. This can be done by including the version number in the REST API URL or the gRPC request.

  For example, in the REST API, you can specify the version number in the URL path like this:

  v1/models/{model_name}/versions/{version_number}:predict

  In a gRPC request, you can set the version number as a custom metadata field.

  TensorFlow Serving keeps track of the loaded model versions and their associated metadata, such as creation time and status. It maintains a model server that serves the specified versions of the models based on the incoming requests.

  When deploying a new model version, you can load it alongside the existing versions. TensorFlow Serving allows you to easily manage the lifecycle of models, including loading, unloading, and managing the serving status. Once a new version is loaded and ready, you can start routing traffic to it without any downtime or disruption to the existing models.

  Overall, TensorFlow Serving provides a flexible and robust system for handling model versioning, allowing you to easily update and serve multiple versions of your machine learning models.