Silky Arora developed robust checkpointing functionality for the keras-team/keras-rs repository, focusing on Keras3 Jax TPU Embeddings. Using Python and leveraging deep learning frameworks such as JAX and Orbax, Silky implemented a custom CheckpointManager and integrated it with Keras training loops via a Callback. This approach enabled seamless saving and restoring of embeddings, metrics, and training state, allowing experiments on TPUs to resume efficiently after interruptions. The work addressed the need for reliable state management in long-running machine learning workflows, demonstrating depth in model checkpointing and integration with existing infrastructure while minimizing disruption to established training processes.