TensorFlow Lite is primarily designed for deploying pre-trained machine learning models on mobile and embedded devices. It is not specifically built for handling model updates and retraining, but rather for efficient inference with existing models. However, there are strategies you can adopt to handle model updates and retraining with TensorFlow Lite.

  1. Model updates: When a new model version is available, you can replace the old model file with the updated one in the mobile app. This can be done by downloading the updated model from a server or integrating it into an app update. TensorFlow Lite allows you to load the new model and use it for inference without modifying your code or app structure.

  2. Retraining: If you want to retrain a model with new data on the device itself, TensorFlow Lite does not provide direct support for this. However, you can implement a pipeline using TensorFlow Lite and TensorFlow itself. Here's a general approach:

   a. Load the pre-trained model using TensorFlow Lite and perform inference on the device.

   b. Collect new data on the device and store it.

   c. Periodically transfer the collected data from the device to a server or a more powerful machine where TensorFlow is installed.

   d. Use the transferred data to retrain the model using TensorFlow, incorporating the new information.

   e. Once the retraining is complete, export the updated model in TensorFlow Lite format.

   f. Replace the old model in the mobile app with the updated one, following the process described earlier.

  It is important to note that TensorFlow Lite has limited capabilities compared to the full TensorFlow framework, as it is optimized for deployment on resource-constrained devices. Therefore, handling more complex tasks like model updates and retraining might require additional custom implementation.

  Overall, TensorFlow Lite focuses on efficient inference, and external tools and workflows are required for managing model updates and retraining.