TensorFlow Serving provides a flexible and scalable solution to handle model updates and retraining. When it comes to model updates, there are a few different approaches you can take:

  1. Update the model file directly: One way to update a model is by replacing the existing model file with a new one. TensorFlow Serving supports hot-reloading of models, meaning you can replace the model file while the serving system is still running. Once the new model file is in place, TensorFlow Serving automatically detects the change and starts serving the updated model.

  2. Versioning: Another approach is to maintain multiple versions of the model and use versioning to switch between them. This allows you to keep older versions of the model active while gradually transitioning to a new version. TensorFlow Serving allows you to specify the version of the model you want to serve, enabling easy rollback if needed.

  Regarding retraining, TensorFlow Serving itself is not responsible for model retraining. It primarily focuses on serving the models. Model retraining is usually performed separately, offline, using TensorFlow or similar frameworks. Once the retraining is complete, you can follow the model update process mentioned earlier to deploy the updated model with TensorFlow Serving.

  It's worth noting that TensorFlow Serving integrates well with TensorFlow's model exporting utilities, such as `tf.saved_model.save()` and `tf.compat.v1.saved_model.simple_save()`. These utilities help in exporting trained models to a format that TensorFlow Serving can understand.

  In summary, TensorFlow Serving provides various options to handle model updates, including replacing the model file or using versioning. However, model retraining is outside the scope of TensorFlow Serving, and it relies on separate tools and frameworks for that purpose.