Continual Learning

Continual learning is an AI model’s ability to keep learning from new data over time without forgetting what it already knows. It’s key for systems that need to adapt in real-time or in changing environments.

Key aspects of continual learning include:

• Ongoing Adaptation: Models update with new inputs instead of retraining from scratch.
• Memory Retention: New learning doesn’t erase previous knowledge (avoids “catastrophic forgetting”).
• Efficiency: Uses data incrementally, saving compute and time.
• Infrastructure Needs: Requires smart retraining pipelines and versioning.
  Business Value: Keeps models accurate and relevant—especially for fast-moving products.
  
  

Continual learning powers AI that stays sharp, saves costs, and evolves alongside your users.