Category: ai-technology | no-code
By Jensen Gardner

Perpetual ML

Advantages:
- Continuous learning: AI Perpetual ML allows models to learn and improve continuously from new data over time.
- Adaptability: It can adapt to changing environments and evolving patterns, making it suitable for dynamic applications.
- Reduced manual intervention: Automation of the retraining process minimizes the need for human oversight and intervention.
- Improved performance: By constantly learning, the model can maintain high accuracy and relevance in predictions.

Disadvantages:
- Data dependency: It requires a constant flow of high-quality data for effective learning, which can be challenging to maintain.
- Complexity: Managing and monitoring perpetual learning models adds complexity to system implementation and deployment.
- Risk of overfitting: Continuous learning can lead to overfitting on recent data if not managed properly.
- Resource-intensive: It may require significant computational resources and infrastructure for ongoing training and adaptation.