What is the difference between stateful and stateless training?

The difference between stateful and stateless training lies in how the model handles new data during training.

Stateless Training: In stateless training, the model is first trained on the original dataset, and then it is fully retrained each time new data becomes available. This is a traditional approach and is often called "stateless retraining."

Stateful Training: In stateful training, the model is initially trained on a batch of data, and instead of being retrained from scratch, it is incrementally updated as new data arrives. This approach allows the model to retain and build on its previous knowledge.