Continual Learning

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Continual learning is used when a model needs to keep improving after deployment. Real environments change. New kinds of data appear, user behavior shifts, and yesterday’s patterns stop being reliable. Continual learning updates the model over time so it stays useful, while trying to keep earlier abilities working instead of degrading with each update.

This is different from one-time training or simple fine-tuning done as a single refresh. Continual learning treats updates as an ongoing process and tracks what is gained and what is lost after each change. Teams often measure it by how well the model learns new conditions while still performing on earlier ones. The goal is steady improvement without older skills quietly disappearing.

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