ML.Net TimeSeriesPrediction model Checkpoint vs retraining the model

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Being pretty new to ML.Net and working with TimeSeriesPrediction models and engine, I don't quite get and find enough documentation about what exactly TimeSeriesPredictionEngine<TSrc,TDst>.CheckPoint method does.
What I understand is that when we have a new feed in the time series we should checkpoint the model for next prediction, but can we keep doing this forever? Do we eventually need to retrain the model after a while or using the Checkpoint methods does the same thing as retraining the model with all the history? What are the best practices in your experience?

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